CI/DC in MLOps by J.B. Hunt

•

1 like•2,854 views

CI/DC in MLOps by J.B. Hunt - Implementing a Framework for Self-Service Experimentation and Deployment at Scale by Wesly Clark and Cara Phillips

Data & Analytics

CI/CD in MLOps
Wesly Clark and Cara Phillips
In partnership with
Implementing a Framework for Self-Service
Experimentation and Deployment at Scale

2
J.B. HUNT AT-A-GLANCE
400+
locations across
U.S., Canada,
Mexico, & Asia
17,000+
tractors
and ICs
133,000+
trailing units
30,000+
employees
$9B
trailing 12-
month
revenue
94,000+
carriers
59-year legacy as a leader in the transportation and logistics industry

WE HELP
Organizations understand how emerging digital technologies
can enable new business models and transform existing one
Intelligent Applications
AI & Machine Learning
Internet of Things
BI & Big Data
WE BRING
Deep expertise across the Microsoft data analytics,
application development and productivity platforms to enable
new ways of work
Recent Case Studies
WE DELIVER
Engagements to help organizations accomplish their
business goals
Projects Strategy Training
Quick
Starts
Design
Sprints
Managed
Services
WE BUILD
Solutions that help transform organizations through
improved insight and productivity
artisconsulting.com 972.702.9500 artis_info@artisconsulting.com
Business Intelligence
Big Data
AI & Machine Learning
Intelligent Applications
Power Apps
IoT
Founded in 2002, Artis Consulting is a professional services firm dedicated to delivering technology solutions that provide the right information, to the right person, at the right time.

PRESENTATION AGENDA
J.B. HUNT TRANSPORT
• Mission Statement
• MLOps Lifecycle
• Motivating Values/Concepts
• Architecture Diagrams
• Clusters and Libraries Tech Demo
• Job Deployment Tech Demo
• Recap

Creating the most efficient transportation network in North America
Mission Statement:
Augment
Analysis
Empower
Expertise
Remove
Redundancy
Automate
Action
Detect
Direction
Predict
Possibilities

MLOps High Level Lifecycle
ML Environment
Creation
Data Analysis
Feature/Model
Selection
Model
Registration
Experimentation/
Monitoring
Job Deployment/
(Re)Training
Model
Deployment/
Serving
Data Expectations/
Feature Store
Data Collection/
Ingestion
Continuously Develop/Iterate Models

Motivating Values/Concepts
Predictable Repeatable
Automated Clearly Defined
Environments
Secure/Self-Service Platform and
Cloud Agnostic

Framework Architecture Diagrams

PRESENTATION AGENDA
J.B. HUNT TRANSPORT
• Mission Statement
• MLOps Lifecycle
• Motivating Values/Concepts
• Architecture Diagrams
• Clusters and Libraries Tech Demo
• Job Deployment Tech Demo
• Recap

THANK YOU

More Related Content

What's hot

MLOps in action

MLOps in action

MLOps in action

Pieter de Bruin

Databricks Overview for MLOps

Databricks Overview for MLOps

Databricks Overview for MLOps

MLOps by Sasha Rosenbaum

MLOps by Sasha Rosenbaum

MLOps by Sasha Rosenbaum

Sasha Rosenbaum

MLOps with Kubeflow

MLOps with Kubeflow

MLOps with Kubeflow

Saurabh Kaushik

The key challenge in making AI technology more accessible to the broader community is the scarcity of AI experts. Most businesses simply don’t have the much needed resources or skills for modeling and engineering. This is why automated machine learning and deep learning technologies (AutoML and AutoDL) are increasingly valued by academics and industry. The core of AI is the model design. Automated machine learning technology reduces the barriers to AI application, enabling developers with no AI expertise to independently and easily develop and deploy AI models. Automated machine learning is expected to completely overturn the AI industry in the next few years, making AI ubiquitous.

AutoML - The Future of AI

AutoML - The Future of AI

AutoML - The Future of AI

In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful. We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?” The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.

Experimentation to Industrialization: Implementing MLOps

Experimentation to Industrialization: Implementing MLOps

Experimentation to Industrialization: Implementing MLOps

MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.

MLOps Using MLflow

MLOps Using MLflow

MLOps Using MLflow

<p>Last summer, Databricks launched MLflow, an open source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs and model packaging. MLflow has grown quickly since then, with over 120 contributors from dozens of companies, including major contributions from R Studio and Microsoft. It has also gained new capabilities such as automatic logging from TensorFlow and Keras, Kubernetes integrations, and a high-level Java API. In this talk, we’ll cover some of the new features that have come to MLflow, and then focus on a major upcoming feature: model management with the MLflow Model Registry. Many organizations face challenges tracking which models are available in the organization and which ones are in production. The MLflow Model Registry provides a centralized database to keep track of these models, share and describe new model versions, and deploy the latest version of a model through APIs. We’ll demonstrate how these features can simplify common ML lifecycle tasks.</p>

Simplifying Model Management with MLflow

Simplifying Model Management with MLflow

Simplifying Model Management with MLflow

Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure. For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps). Agenda - Data Quality and why it matters - Challenges and solutions of Data Testing - Challenges and solutions of Model Testing - MLOps pipelines and why they matter - How to expand validation pipelines for Data Quality

MLOps and Data Quality: Deploying Reliable ML Models in Production

MLOps and Data Quality: Deploying Reliable ML Models in Production

MLOps and Data Quality: Deploying Reliable ML Models in Production

In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before. The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups. Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.

Using MLOps to Bring ML to Production/The Promise of MLOps

Using MLOps to Bring ML to Production/The Promise of MLOps

Using MLOps to Bring ML to Production/The Promise of MLOps

Managing the Machine Learning Lifecycle with MLflow

Managing the Machine Learning Lifecycle with MLflow

Managing the Machine Learning Lifecycle with MLflow

Ml ops intro session

Ml ops intro session

Ml ops intro session

MLOps with Azure DevOps

MLOps with Azure DevOps

MLOps with Azure DevOps

Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.

Seamless MLOps with Seldon and MLflow

Seamless MLOps with Seldon and MLflow

Seamless MLOps with Seldon and MLflow

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

MLOps – Applying DevOps to Competitive Advantage

MLOps – Applying DevOps to Competitive Advantage

Introducing MLOps.pdf

Introducing MLOps.pdf

Introducing MLOps.pdf

Dr. Anish Cheriyan (PhD)

Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder. In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.

mlflow: Accelerating the End-to-End ML lifecycle

mlflow: Accelerating the End-to-End ML lifecycle

mlflow: Accelerating the End-to-End ML lifecycle

Managing the Machine Learning Lifecycle with MLOps

Managing the Machine Learning Lifecycle with MLOps

Managing the Machine Learning Lifecycle with MLOps

What is MLOps

Henrik Skogström

Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps. https://dataphoenix.info/the-a-z-of-data/ https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/

The A-Z of Data: Introduction to MLOps

The A-Z of Data: Introduction to MLOps

The A-Z of Data: Introduction to MLOps

What's hot (20)

MLOps in action

MLOps in action

MLOps in action

Databricks Overview for MLOps

Databricks Overview for MLOps

Databricks Overview for MLOps

MLOps by Sasha Rosenbaum

MLOps by Sasha Rosenbaum

MLOps by Sasha Rosenbaum

MLOps with Kubeflow

MLOps with Kubeflow

MLOps with Kubeflow

AutoML - The Future of AI

AutoML - The Future of AI

AutoML - The Future of AI

Experimentation to Industrialization: Implementing MLOps

Experimentation to Industrialization: Implementing MLOps

Experimentation to Industrialization: Implementing MLOps

MLOps Using MLflow

MLOps Using MLflow

MLOps Using MLflow

Simplifying Model Management with MLflow

Simplifying Model Management with MLflow

Simplifying Model Management with MLflow

MLOps and Data Quality: Deploying Reliable ML Models in Production

MLOps and Data Quality: Deploying Reliable ML Models in Production

MLOps and Data Quality: Deploying Reliable ML Models in Production

Using MLOps to Bring ML to Production/The Promise of MLOps

Using MLOps to Bring ML to Production/The Promise of MLOps

Using MLOps to Bring ML to Production/The Promise of MLOps

Managing the Machine Learning Lifecycle with MLflow

Managing the Machine Learning Lifecycle with MLflow

Managing the Machine Learning Lifecycle with MLflow

Ml ops intro session

Ml ops intro session

Ml ops intro session

MLOps with Azure DevOps

MLOps with Azure DevOps

MLOps with Azure DevOps

Seamless MLOps with Seldon and MLflow

Seamless MLOps with Seldon and MLflow

Seamless MLOps with Seldon and MLflow

MLOps – Applying DevOps to Competitive Advantage

MLOps – Applying DevOps to Competitive Advantage

MLOps – Applying DevOps to Competitive Advantage

Introducing MLOps.pdf

Introducing MLOps.pdf

Introducing MLOps.pdf

mlflow: Accelerating the End-to-End ML lifecycle

mlflow: Accelerating the End-to-End ML lifecycle

mlflow: Accelerating the End-to-End ML lifecycle

Managing the Machine Learning Lifecycle with MLOps

Managing the Machine Learning Lifecycle with MLOps

Managing the Machine Learning Lifecycle with MLOps

What is MLOps

The A-Z of Data: Introduction to MLOps

The A-Z of Data: Introduction to MLOps

The A-Z of Data: Introduction to MLOps

Similar to CI/DC in MLOps by J.B. Hunt

Insights Success is The Best Business Magazine in the world for enterprises. Being a platform, it focuses distinctively on emerging as well as leading fastest growing companies, their confrontational style of doing businesses and the way of delivering effective and collaborative solutions to strengthen market share. Here, we talk about the leader’s viewpoints & ideas, latest products/services, etc. Insights Success magazine reaches out to all the ‘C’ Level Professionals, VPs, Consultants, VCs, Managers, and HRs of various industries.

Insight success the 10 most admired companies in cloud computing oct 2017

Insight success the 10 most admired companies in cloud computing oct 2017

Insight success the 10 most admired companies in cloud computing oct 2017

Insights success media and technology pvt ltd

Gillette Stadium Cloud Event

Gillette Stadium Cloud Event

Gillette Stadium Cloud Event

About Excella

T-Byte Consulting & IT Services March 2021

T-Byte Consulting & IT Services March 2021

T-Byte Consulting & IT Services March 2021

The 10 best enterprise collaboration solution providers dec 2017

The 10 best enterprise collaboration solution providers dec 2017

The 10 best enterprise collaboration solution providers dec 2017

Employment trends 2011 by zylog

Employment trends 2011 by zylog

Employment trends 2011 by zylog

Zylog Systems Canada Ltd

ATSPL '24: Empowering Success and Growth

ATSPL '24: Empowering Success and Growth

ATSPL '24: Empowering Success and Growth

Aishka Technology Solutions Private Limited

Aishka Technology Solutions Private Limited

Aishka Technology Solutions Private Limited

Miracle Software Systems, Inc. is a Global Systems Integrator headquartered out of Novi, MI USA. With over 2000 employees located across the globe in 4 continents, Miracle has been helping its customers transform their enterprise to become the ultimate “Connected Digital Enterprise”. For over two decades Miracle has been the industry’s Thought Leader and Innovator, providing our customers with Innovative Solutions at affordable costs. Our unique Hybrid Delivery System combined with our established Talent Ecosystem, provides customers with a model setup for Affordable Innovation. For more information visit, www.miraclesoft.com

MiracleSoft Corporate Profile

Daniel R. Dugan

Miracle Software Systems, Inc. is a Global Systems Integrator headquartered out of Novi, MI USA. With over 2000 employees located across the globe in 4 continents, Miracle has been helping its customers transform their enterprise to become the ultimate “Connected Digital Enterprise”. For over two decades Miracle has been the industry’s Thought Leader and Innovator, providing our customers with Innovative Solutions at affordable costs. Our unique Hybrid Delivery System combined with our established Talent Ecosystem, provides customers with a model setup for Affordable Innovation. For more information visit, www.miraclesoft.com

MiracleSoft profile

Daniel R. Dugan

Miracle Corporate profile

Top 10 companies leading the cloud revolution 2021 compressed

Top 10 companies leading the cloud revolution 2021 compressed

Top 10 companies leading the cloud revolution 2021 compressed

Insights success media and technology pvt ltd

Digital. The word is permeating every organization and industry today. Digitally transforming your business is increasingly critical to continued growth, but the path to digitalization isn’t always well-defined. What does reimagining your business in this digital age really mean? You should be assessing your capabilities around the four “megatrends” of cloud, mobile, social, and analytics development, and creating a strategy and a road map to improve and differentiate core capabilities leveraging these digital technologies. Our webinar demonstrated how you can use Microsoft technology to create a holistic digital transformation across the enterprise, and embrace the four megatrends to increase productivity, improve customer service, expand market share, and ultimately, increase revenue.

Reimagine Your Business in a Digital-First World with Microsoft

Reimagine Your Business in a Digital-First World with Microsoft

Reimagine Your Business in a Digital-First World with Microsoft

Perficient, Inc.

T Bytes Consulting & IT Services

T Bytes Consulting & IT Services

T Bytes Consulting & IT Services

Valuebound Inc. a custom web solutions and services partner, is managed by a team of talented professionals who have astute business acumen and cumulative expertise in providing the right IT solutions. We have crafted web solutions for all size businesses in a wide range of domains: from retail to manufacturing, from travel to real estate and architecture. With experience and dedication, we have accumulated the knowledge and skills we bring to bear in the business success of each client.

Valuebound Company Presentation

Valuebound Company Presentation

Valuebound Company Presentation

Chordify Brochure Staffing

Chordify Brochure Staffing

Chordify Brochure Staffing

This month, Insights Success is bringing its readers, the stories which belong to the Indian digital space. In this issue of ‘The 10 Most Prominent Google Partners to look at in 2019,’ the magazine is showcasing exemplary work done by some of the best Google Partners in the country. On the cover, we have featured CGS Infotech Solutions Pvt. Ltd. It is an authorized partner for Google G Suite, Google Trusted Street View Agency for VR, and the pioneering partner in India for Google AdWords

The 10 most prominent analytics companies to watch

The 10 most prominent analytics companies to watch

The 10 most prominent analytics companies to watch

AVANI-Capabilities-Statement

AVANI-Capabilities-Statement

AVANI-Capabilities-Statement

Sameer Penakalapati

The future of the cloud holds a wealth of promise for those who know how to leverage the power of high-performance computing to fuel business innovation and growth. As predicted, cloud has quickly become a prominent technology for executing digital transformation, but many enterprises are still struggling to understand how the cloud can help future-proof their business. This second webinar in our Cloud First, Business-Driven webinar series explored some of the key concepts around the future of cloud, and how to think about what’s next for your enterprise. We discussed: -Short- and long-term cloud trends -Personal cloud with intelligent agent -Personalized pricing and payment systems -Taking hybrid cloud to the next level -Customer-defined products

Navigating the Future of the Cloud to Fuel Innovation

Navigating the Future of the Cloud to Fuel Innovation

Navigating the Future of the Cloud to Fuel Innovation

Perficient, Inc.

The 10 most valuable aws solution providers dec 2017

The 10 most valuable aws solution providers dec 2017

The 10 most valuable aws solution providers dec 2017

Similar to CI/DC in MLOps by J.B. Hunt (20)

Insight success the 10 most admired companies in cloud computing oct 2017

Insight success the 10 most admired companies in cloud computing oct 2017

Insight success the 10 most admired companies in cloud computing oct 2017

Gillette Stadium Cloud Event

Gillette Stadium Cloud Event

Gillette Stadium Cloud Event

About Excella

T-Byte Consulting & IT Services March 2021

T-Byte Consulting & IT Services March 2021

T-Byte Consulting & IT Services March 2021

The 10 best enterprise collaboration solution providers dec 2017

The 10 best enterprise collaboration solution providers dec 2017

The 10 best enterprise collaboration solution providers dec 2017

Employment trends 2011 by zylog

Employment trends 2011 by zylog

Employment trends 2011 by zylog

ATSPL '24: Empowering Success and Growth

ATSPL '24: Empowering Success and Growth

ATSPL '24: Empowering Success and Growth

Aishka Technology Solutions Private Limited

Aishka Technology Solutions Private Limited

Aishka Technology Solutions Private Limited

MiracleSoft Corporate Profile

MiracleSoft profile

Miracle Corporate profile

Top 10 companies leading the cloud revolution 2021 compressed

Top 10 companies leading the cloud revolution 2021 compressed

Top 10 companies leading the cloud revolution 2021 compressed

Reimagine Your Business in a Digital-First World with Microsoft

Reimagine Your Business in a Digital-First World with Microsoft

Reimagine Your Business in a Digital-First World with Microsoft

T Bytes Consulting & IT Services

T Bytes Consulting & IT Services

T Bytes Consulting & IT Services

Valuebound Company Presentation

Valuebound Company Presentation

Valuebound Company Presentation

Chordify Brochure Staffing

Chordify Brochure Staffing

Chordify Brochure Staffing

The 10 most prominent analytics companies to watch

The 10 most prominent analytics companies to watch

The 10 most prominent analytics companies to watch

AVANI-Capabilities-Statement

AVANI-Capabilities-Statement

AVANI-Capabilities-Statement

Navigating the Future of the Cloud to Fuel Innovation

Navigating the Future of the Cloud to Fuel Innovation

Navigating the Future of the Cloud to Fuel Innovation

The 10 most valuable aws solution providers dec 2017

The 10 most valuable aws solution providers dec 2017

The 10 most valuable aws solution providers dec 2017

More from Databricks

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs. There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs. The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

More from Databricks (20)

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Recently uploaded

Determinants of health, dimensions of health, positive health and spectrum of...

Determinants of health, dimensions of health, positive health and spectrum of...

Determinants of health, dimensions of health, positive health and spectrum of...

shambhavirathore45

Zuja dropshipping via API with DroFx.pptx

Zuja dropshipping via API with DroFx.pptx

Zuja dropshipping via API with DroFx.pptx

olyaivanovalion

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage with sex Booking Contact Details :- WhatsApp Chat :- +91-9711199171 27-April-2024(SMW) Best Escorts Service in Delhi Call Us — 9711199171 —Are you Looking for Call Girls in Delhi then This's a perfect place for you. We brings Many Models and Independent Call girls who Can fulfill all your Sexual Desires by Their Complete package of sex Service and It's Absolutely safe and secure. Book Your One night Stand Call Girls : 9711199171 -SERVICES- Our Delhi Call Girls are fully cooperative and understand your needs and they Give you their complete package of Sex service like- Real girl friend experience, Lip Kiss, Lip Lock, Smooch, Sucking without Condom, Oral, Erotic Massage, Lap Dance, Threesome, 69 Licking, Sex in all Position, Anal Sex etc, -AVAILABLE GIRLS- We Bring Many Good looking Slim, Busty, Hot and Sexy Call Girls as per our client's Choice like Housewives, College girls, Russian girls, Muslim girls, Afghani girls, Bengali girls, Working girls, south Indian girls, Punjabi girls, Big Boobs Call Girls etc, -PAYMENT METHOD- C O D. If you Want to Book our Call girls Service in Delhi . We don,t ask anything in Advance, you Can simply pay to her Cash on Delivery or via any upi. In-Call: — You can reach at our Place in Delhi Our place Hotel and Flat Which Is Very Clean Hygiene And 100% safe Accommodation. Out-Call: — You Can pick up the Girl from my Place if not we Provide Door-Step Call Girls Delivery in Delhi. NOTE: — Pic Collectors Time Passers and Bargainers please Stay Away As We Respect The Value For Your Money Time And Expect The Same From You. OUR SERVICES RATE: – let you know the our Call girls price in Delhi are very affordable and Best rate in genuine market of Call girls. But as all you knows that Quality has comes with its own Price tag. One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9711199171 We are available 24*7 all days of the year. Call us — 9711199171 Thank you for Visiting.

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

shivangimorya083

Call Girls In Connaught Place Delhi Call or Whataap 🔝 9953056974 🔝Escorts provide 24×7 Available With Room TIMINGS 24 HOURS OPENS Booking Now Gentleman Only:-Call Now Best High Class Normal Call Girls Escorts Service In Delhi NCR 24-7 Hours Available Service I, provide In Delhi NCR Female Escorts Sex Service 100% Customers Satisfaction Guarantee VIP Profiles Top Grade Service 100% Cooperative All round Service 🔝 9953056974 🔝 InCall: – You Can Reach At Our Place in Delhi Our place Which Is Very Clean Hygienic 100% safe Accommodation OutCall: – Service For Out Call You have To Come Pick The Girl From My Place We Also Provide Door Step Services Note: – Pic Collectors Time Passers Bargainers Stay Away As We Respect The Value For Your Money Time And Expect The Same From You 🔝 9953056974 🔝 Hygienic: – Full Ac Neat And Clean Rooms Available In Hotel 24 * 7 Hrs In Delhi Ncr 🔝 9953056974 🔝 Place: – South Extension Nehru Place Saket Malviya Nagar Munirka Vasant Kunj Safdarjung Katwaria Sarai Lajpat Nagar Kalkaji Hauz Khas Mahipalpur Dwarka Karol Bagh Noida Gurgaon Faridabad All Outcall Only Hotel Service In Delhi Ncr 🔝 9953056974 🔝 We Are Providing : – House Wife’s : – Private Independent House Wife’ : – Private Independent Collage Going Girls : – Corporate MNC Working Profiles : – Call Center Girls: – Live Band Girls : – Foreigners Many More: – Independent Models Service type For Pics And Other Details Pls Whatsapp Me Otherwise Call Me Any Time Incall Outcall Both Are Services Available Door Step, home, Apartment, Guest House, Flate, All Star Hotel Available 99530 vip 56974 THANKS FOR VISITING Booking 24×7 HRS

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Halmar dropshipping via API with DroFx

Halmar dropshipping via API with DroFx

Halmar dropshipping via API with DroFx

olyaivanovalion

Smarteg dropshipping via API with DroFx.pptx

Smarteg dropshipping via API with DroFx.pptx

Smarteg dropshipping via API with DroFx.pptx

olyaivanovalion

"Dive into the dynamic landscapes of Southeast Asia's top economic powerhouses with this comprehensive market analysis. Through the lens of a fictitious company, explore the intricate nuances of consumer behavior, industry trends, and economic indicators in the region's key players: Indonesia, Thailand, Malaysia, Singapore, and Vietnam. From GDP growth projections to sector-specific insights, uncover actionable intelligence to drive strategic decisions and capitalize on emerging opportunities in this vibrant market."

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Rachmat Ramadhan H

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call Booking Contact Details :- WhatsApp Chat :- +91-9711199171 27-April-2024(SMW) Best Escorts Service in Delhi Call Us — 9711199171 —Are you Looking for Call Girls in Delhi then This's a perfect place for you. We brings Many Models and Independent Call girls who Can fulfill all your Sexual Desires by Their Complete package of sex Service and It's Absolutely safe and secure. Book Your One night Stand Call Girls : 9711199171 -SERVICES- Our Delhi Call Girls are fully cooperative and understand your needs and they Give you their complete package of Sex service like- Real girl friend experience, Lip Kiss, Lip Lock, Smooch, Sucking without Condom, Oral, Erotic Massage, Lap Dance, Threesome, 69 Licking, Sex in all Position, Anal Sex etc, -AVAILABLE GIRLS- We Bring Many Good looking Slim, Busty, Hot and Sexy Call Girls as per our client's Choice like Housewives, College girls, Russian girls, Muslim girls, Afghani girls, Bengali girls, Working girls, south Indian girls, Punjabi girls, Big Boobs Call Girls etc, -PAYMENT METHOD- C O D. If you Want to Book our Call girls Service in Delhi . We don,t ask anything in Advance, you Can simply pay to her Cash on Delivery or via any upi. In-Call: — You can reach at our Place in Delhi Our place Hotel and Flat Which Is Very Clean Hygiene And 100% safe Accommodation. Out-Call: — You Can pick up the Girl from my Place if not we Provide Door-Step Call Girls Delivery in Delhi. NOTE: — Pic Collectors Time Passers and Bargainers please Stay Away As We Respect The Value For Your Money Time And Expect The Same From You. OUR SERVICES RATE: – let you know the our Call girls price in Delhi are very affordable and Best rate in genuine market of Call girls. But as all you knows that Quality has comes with its own Price tag. One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9711199171 We are available 24*7 all days of the year. Call us — 9711199171 Thank you for Visiting.

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

shivangimorya083

How fast can you modify your data collection to include a new field, make all the necessary changes in data processing and storage, and then use that field in analytics or product features? For many companies, the answer is a few quarters, whereas others do it in a day. This data agility latency has a direct impact on companies' ability to innovate with data. Schema-on-read has been a key strategy to lower that latency - as the community has shifted towards storing data outside relational databases, we no longer need to make series of schema changes through the whole data chain, coordinated between teams to minimise operational risk. Schema-on-read comes with a cost, however. Errors that we used to catch during testing or in early test deployments can now sneak into production undetected and surface as product errors or hard-to-debug data quality problems later than with schema-on-write solutions. In this presentation, we will show how we have rejected the tradeoff between slow schema change rate and quality to achieve the best of both worlds. By using metaprogramming and versioned pipelines that are tested end-to-end, we can achieve fast schema changes with schema-on-write and the protection of static typing. We will describe the tools in our toolbox - Scalameta, Chimney, Bazel, and custom tools. We will also show how we leverage them to take static typing one step further and differentiate between domain types that share representation, e.g. EmailAddress vs ValidatedEmailAddress or kW vs kWh, while maintaining harmony with data technology ecosystems.

Schema on read is obsolete. Welcome metaprogramming..pdf

Schema on read is obsolete. Welcome metaprogramming..pdf

Schema on read is obsolete. Welcome metaprogramming..pdf

Lars Albertsson

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Mature dropshipping via API with DroFx.pptx

Mature dropshipping via API with DroFx.pptx

Mature dropshipping via API with DroFx.pptx

olyaivanovalion

Ravak dropshipping via API with DroFx.pptx

Ravak dropshipping via API with DroFx.pptx

Ravak dropshipping via API with DroFx.pptx

olyaivanovalion

Call Girl In Dwarka ☎92055#41914 ¶¶ Indian,Russian Best Quality full Educated And Full Cooperative Independent Call Girls Escort Services In New Delhi- I Have Extremely Beautiful Broad Minded Cute Sexy & Hot Call Girls and Escorts, We Are Located in 3* 4* 5* Hotels in Delhi. Safe & Secure High Class Services Affordable Rate 100% Satisfaction, Unlimited Enjoyment. Any Time for Model/Teens Escort in Delhi High class luxury and premium escorts agency Indian Russian Call Girls In Delhi Booking Good High Profile Escorts (Call Girls) In Delhi 5 Star Hotel ,Incall Service,OutCall Service, We provide services by Call Girls,College Girls,Modals Get High Profile queens,Well Educated,Good Looking,Full Cooperative Model, Russian Models,Punjabi Girls Kashmeri Girls Services etc… We Provide Hottest Female With Safe And Consensual With Most Limits Respected Complete Satisfaction Guaranteed…Service. Call Me Spacial For Including Incall//outcall Service In New Delhi Indian Russian Escorts Service,

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Delhi Call girls

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medici

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

Unlock the Secrets of Discriminative Models: Master the Art of Prediction! 🌟 Dive into our latest presentation where we simplify the complex world of machine learning. Discover how discriminative models, from Logistic Regression to Neural Networks, optimize decision-making by focusing on the relationships between observed features and outcomes. Perfect for beginners and experts alike! 🚀 #MachineLearning #DataScience #AI #PredictiveAnalytics #100ConceptsOfAIwithAnupamaK

100-Concepts-of-AI by Anupama Kate .pptx

100-Concepts-of-AI by Anupama Kate .pptx

100-Concepts-of-AI by Anupama Kate .pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

MohammedJunaid861692

Recently uploaded (20)

Determinants of health, dimensions of health, positive health and spectrum of...

Determinants of health, dimensions of health, positive health and spectrum of...

Determinants of health, dimensions of health, positive health and spectrum of...

Zuja dropshipping via API with DroFx.pptx

Zuja dropshipping via API with DroFx.pptx

Zuja dropshipping via API with DroFx.pptx

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

Vip Model Call Girls (Delhi) Karol Bagh 9711199171✔️Body to body massage wit...

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Hsr Layout Just Call 👗 7737669865 👗 Top Class Call Girl Service Ba...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Halmar dropshipping via API with DroFx

Halmar dropshipping via API with DroFx

Halmar dropshipping via API with DroFx

Smarteg dropshipping via API with DroFx.pptx

Smarteg dropshipping via API with DroFx.pptx

Smarteg dropshipping via API with DroFx.pptx

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

Delhi Call Girls Punjabi Bagh 9711199171 ☎✔👌✔ Whatsapp Hard And Sexy Vip Call

Schema on read is obsolete. Welcome metaprogramming..pdf

Schema on read is obsolete. Welcome metaprogramming..pdf

Schema on read is obsolete. Welcome metaprogramming..pdf

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

Mature dropshipping via API with DroFx.pptx

Mature dropshipping via API with DroFx.pptx

Mature dropshipping via API with DroFx.pptx

Ravak dropshipping via API with DroFx.pptx

Ravak dropshipping via API with DroFx.pptx

Ravak dropshipping via API with DroFx.pptx

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

100-Concepts-of-AI by Anupama Kate .pptx

100-Concepts-of-AI by Anupama Kate .pptx

100-Concepts-of-AI by Anupama Kate .pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

BPAC WITH UFSBI GENERAL PRESENTATION 18_05_2017-1.pptx

CI/DC in MLOps by J.B. Hunt

1. CI/CD in MLOps Wesly Clark and Cara Phillips In partnership with Implementing a Framework for Self-Service Experimentation and Deployment at Scale

2. 2 J.B. HUNT AT-A-GLANCE 400+ locations across U.S., Canada, Mexico, & Asia 17,000+ tractors and ICs 133,000+ trailing units 30,000+ employees $9B trailing 12- month revenue 94,000+ carriers 59-year legacy as a leader in the transportation and logistics industry

3. WE HELP Organizations understand how emerging digital technologies can enable new business models and transform existing one Intelligent Applications AI & Machine Learning Internet of Things BI & Big Data WE BRING Deep expertise across the Microsoft data analytics, application development and productivity platforms to enable new ways of work Recent Case Studies WE DELIVER Engagements to help organizations accomplish their business goals Projects Strategy Training Quick Starts Design Sprints Managed Services WE BUILD Solutions that help transform organizations through improved insight and productivity artisconsulting.com 972.702.9500 artis_info@artisconsulting.com Business Intelligence Big Data AI & Machine Learning Intelligent Applications Power Apps IoT Founded in 2002, Artis Consulting is a professional services firm dedicated to delivering technology solutions that provide the right information, to the right person, at the right time.

4. PRESENTATION AGENDA J.B. HUNT TRANSPORT • Mission Statement • MLOps Lifecycle • Motivating Values/Concepts • Architecture Diagrams • Clusters and Libraries Tech Demo • Job Deployment Tech Demo • Recap

5. Creating the most efficient transportation network in North America Mission Statement: Augment Analysis Empower Expertise Remove Redundancy Automate Action Detect Direction Predict Possibilities

6. MLOps High Level Lifecycle ML Environment Creation Data Analysis Feature/Model Selection Model Registration Experimentation/ Monitoring Job Deployment/ (Re)Training Model Deployment/ Serving Data Expectations/ Feature Store Data Collection/ Ingestion Continuously Develop/Iterate Models

7. Motivating Values/Concepts Predictable Repeatable Automated Clearly Defined Environments Secure/Self-Service Platform and Cloud Agnostic

8. Framework Architecture Diagrams

9. PRESENTATION AGENDA J.B. HUNT TRANSPORT • Mission Statement • MLOps Lifecycle • Motivating Values/Concepts • Architecture Diagrams • Clusters and Libraries Tech Demo • Job Deployment Tech Demo • Recap