SlideShare a Scribd company logo
1 of 14
Download to read offline
Building a MLOps platform
around mlflow to enable model
productionalization in just a few
minutes
Milan Berka
ML Engineer @ DataSentics
Agenda
I. Few words about DataSentics
II. Motivation - common data analytical problems we
encounter
III. MLOps – how do we understand it, what it means for
getting data scientist’s model into production within
our platform
IV. Demo
V. Lessons learned and next steps
Few words about us
Agile Data Analytics platform
Make data science and machine learning have a real
impact on organizations across the world -
demystify the hype and black magic surrounding
AI/ML and bring to life transparent production-level
data science solutions and products delivering
tangible impact and innovation.
European Data Science Center of Excellence based in Prague
▪ Machine learning and cloud data engineering
boutique
▪ Helping customers build end-to-end data
solutions in cloud
▪ Incubator of ML-based products
▪ 80 specialist (data science, data/software
engineering)
▪ Partner of Databricks & Microsoft
Problems we commonly encounter
At our own data-driven products and at our customers:
To combat these problems, we assembled a dedicated team: TechSentics, whose goal is to systematically scale
the technical quality of data driven products and deliver ML systems which tackle those problems in context of
enterprise security and other processes.
We want a small agile team of data scientists and engineers to be able to prototype new advanced analytics
use cases using diverse internal and external data in weeks and bring them into production in a few months
We need high quality data for our model development, and we need to have access to the data! We want the
feature store!
We don’t want to buy magical AI
black box solutions. We want our
own open platform
We want to focus on the use cases and
logic, not the DevOps and plumbing
We put together a team of data scientists, they create a ML model, everyone
tells how performant it is, but despite this, it never gets beyond the
experimentation phase and never really affects anything.
As data scientists, we feel that software engineers and
software architects sometimes do not understand us! Data
scientists need real production data for the experimentation
and model development. Model != code, need different skills
to make, different tests to apply! Sometimes it takes hours to
get a reasonable model, sometimes it takes weeks or more!
There is a rich cloud-based
service offering that claim to
address the problems, but there
is always some stitching required
to make it work.
Data intensive tooling by TechSentics
Cloud Advanced Data Analytics Platform Feature Store and MLOps
Isolated environments with DevOps
Data lake house tooling & governance
Utilizing Databricks and Azure with portability to
different spark ecosystem
Managing the lifecycle of a model and features
Model & Data & Code as microservices
Unique hybrid delivery mode for large enterprises
Enterprise-ready and automated data analytics in
azure, proven at banks. Open-source soon.
Our flagship to help ML to play a significant role in organizations and keep high SLAs
Our take on MLOps
Set of practices for managing and streamlining machine learning (ML) models' lifecycle - from development
all the way to production.
MLOps topics:
▪ Feature Store - one central place for storing and
managing ML features across the company
▪ Model Registery - place where to store model artifacts and
other necessary files
▪ Model deployment - process of deploying model
▪ Model operations - monitoring / logging / retraining
/ optimizing the model application
▪ Model reproducibility & portability &
interpretability (explainability)
▪ XAI in MLOps (Explain Ops)
▪ Standardization & reusability - template / interface (API,
SDK..) for data scientists to ease the development and
deployment
Our take on MLOps
Demo
Overview
Model Development Assistant:
▪ Provides templates how to structure and develop
machine learning projects - to comply with the rest of
the MLOps module frame.
Model Repository (MLflow framework):
▪ Experimental tracking of metadata (metrics, parameters, tags,
code) and artifacts (models, data) for the data science model
training experiments / trials.
Serving Manager:
▪ Catalog of prepared templates for deployment of the ML models. By default supports Azure
Databricks Spark/Python/R batch/streaming jobs, Docker packaging, deployment of real-time
endpoints on Azure Container Instances; extendable
▪ Management of retraining and release pipelines.
▪ Automatic pipeline triggers: “model stage transition”-based trigger, “model performance”-
based trigger, time-based trigger. Support for custom triggers.
Monitoring and alerting:
▪ Model drift and skew monitoring
▪ Alerting if metrics exceeds a given threshold
Lessons Learned & Next steps
Lessons learned
• MLOps is not easy to adopt, requires a lot of involvement and is still rather open topic
• mlflow in its open source form is quite naked and there is a lot to be developed. Especially surprising is the artifact
store “bypass”
• Data Scientists are not always super-used to some common software practices such as git
• Writing ML tests can be tricky
• Models can be trained directly in Automation tools ☺
• There is a lot of interest from both internal and external teams => we shipped the platform to our customers as well
Next steps for our platform:
- One centralized MLOps module interface & front-end, instead of multiple interfaces for each component (replacing
the need to interact with the Azure DevOps (or other similar provider), mlflow, ACI, Azure Monitor front-ends for
certain parts of the workflow).
- Introduce ExplainOps component, providing explanations for obtained predictions.
- Richer deployment offering (canary deployments, wider target environment type support)
- Tighter feature store integration
Call to action
As mentioned, because of the high interest, we are about to…
… open-source the infrastructural parts of the platform!
Let’s push this forward together!
We are looking for first 2-3 companies willing to use it in challenging
environment & contribute.
Platform Team - kudos
Jan ProchazkaTomas Kresal Anastasia Lebedeva
Jiri Koutny Michal Halenka
Marek Lipan
Nikola Valesova Michal Mlaka
Oldrich Vlasic
Ondrej Havlicek
Jan Perina
Samuel Messa
Thank you
Feedback
Your feedback is important to us.
Don’t forget to rate
and review the sessions.

More Related Content

What's hot

MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
 MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ... MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
Databricks
 
Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflowManaging the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflow
Databricks
 
MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle
Databricks
 

What's hot (20)

MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
 MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ... MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...
 
Databricks Overview for MLOps
Databricks Overview for MLOpsDatabricks Overview for MLOps
Databricks Overview for MLOps
 
MLOps by Sasha Rosenbaum
MLOps by Sasha RosenbaumMLOps by Sasha Rosenbaum
MLOps by Sasha Rosenbaum
 
What is MLOps
What is MLOpsWhat is MLOps
What is MLOps
 
MLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at ScaleMLOps Virtual Event: Automating ML at Scale
MLOps Virtual Event: Automating ML at Scale
 
From Data Science to MLOps
From Data Science to MLOpsFrom Data Science to MLOps
From Data Science to MLOps
 
MLflow Model Serving
MLflow Model ServingMLflow Model Serving
MLflow Model Serving
 
MLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine LearningMLflow: A Platform for Production Machine Learning
MLflow: A Platform for Production Machine Learning
 
MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.MLOps Bridging the gap between Data Scientists and Ops.
MLOps Bridging the gap between Data Scientists and Ops.
 
MLops workshop AWS
MLops workshop AWSMLops workshop AWS
MLops workshop AWS
 
MLflow with Databricks
MLflow with DatabricksMLflow with Databricks
MLflow with Databricks
 
Apply MLOps at Scale by H&M
Apply MLOps at Scale by H&MApply MLOps at Scale by H&M
Apply MLOps at Scale by H&M
 
ML-Ops how to bring your data science to production
ML-Ops  how to bring your data science to productionML-Ops  how to bring your data science to production
ML-Ops how to bring your data science to production
 
MLOps.pptx
MLOps.pptxMLOps.pptx
MLOps.pptx
 
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
 
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life CycleMLflow: Infrastructure for a Complete Machine Learning Life Cycle
MLflow: Infrastructure for a Complete Machine Learning Life Cycle
 
Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflowManaging the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflow
 
Mlflow with databricks
Mlflow with databricksMlflow with databricks
Mlflow with databricks
 
Apply MLOps at Scale
Apply MLOps at ScaleApply MLOps at Scale
Apply MLOps at Scale
 
MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle MLFlow: Platform for Complete Machine Learning Lifecycle
MLFlow: Platform for Complete Machine Learning Lifecycle
 

Similar to Building a MLOps Platform Around MLflow to Enable Model Productionalization in Just a Few Minutes

Infrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload DeploymentInfrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload Deployment
Databricks
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine Learning
Provectus
 

Similar to Building a MLOps Platform Around MLflow to Enable Model Productionalization in Just a Few Minutes (20)

Databricks for MLOps Presentation (AI/ML)
Databricks for MLOps Presentation (AI/ML)Databricks for MLOps Presentation (AI/ML)
Databricks for MLOps Presentation (AI/ML)
 
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
[DSC Europe 23] Petar Zecevic - ML in Production on Databricks
 
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowMohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with Kubeflow
 
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowMohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with Kubeflow
 
Global AI Bootcamp Madrid - Azure Databricks
Global AI Bootcamp Madrid - Azure DatabricksGlobal AI Bootcamp Madrid - Azure Databricks
Global AI Bootcamp Madrid - Azure Databricks
 
Open, Secure & Transparent AI Pipelines
Open, Secure & Transparent AI PipelinesOpen, Secure & Transparent AI Pipelines
Open, Secure & Transparent AI Pipelines
 
Infrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload DeploymentInfrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload Deployment
 
Making Data Science Scalable - 5 Lessons Learned
Making Data Science Scalable - 5 Lessons LearnedMaking Data Science Scalable - 5 Lessons Learned
Making Data Science Scalable - 5 Lessons Learned
 
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
AutoML - Heralding a New Era of Machine Learning - CASOUG Oct 2021
 
Simplifying AI and Machine Learning with Watson Studio
Simplifying AI and Machine Learning with Watson StudioSimplifying AI and Machine Learning with Watson Studio
Simplifying AI and Machine Learning with Watson Studio
 
Integrating Machine Learning Capabilities into your team
Integrating Machine Learning Capabilities into your teamIntegrating Machine Learning Capabilities into your team
Integrating Machine Learning Capabilities into your team
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine Learning
 
Experimentation to Industrialization: Implementing MLOps
Experimentation to Industrialization: Implementing MLOpsExperimentation to Industrialization: Implementing MLOps
Experimentation to Industrialization: Implementing MLOps
 
Training and deploying ML models with Google Cloud Platform
Training and deploying ML models with Google Cloud PlatformTraining and deploying ML models with Google Cloud Platform
Training and deploying ML models with Google Cloud Platform
 
Norman Sasono - Incorporating AI/ML into Your Application Architecture
Norman Sasono - Incorporating AI/ML into Your Application ArchitectureNorman Sasono - Incorporating AI/ML into Your Application Architecture
Norman Sasono - Incorporating AI/ML into Your Application Architecture
 
Norman Sasono - Incorporating AI/ML into Your Application Architecture
Norman Sasono - Incorporating AI/ML into Your Application ArchitectureNorman Sasono - Incorporating AI/ML into Your Application Architecture
Norman Sasono - Incorporating AI/ML into Your Application Architecture
 
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsVertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflows
 
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher ScientificEnabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific
 
The A-Z of Data: Introduction to MLOps
The A-Z of Data: Introduction to MLOpsThe A-Z of Data: Introduction to MLOps
The A-Z of Data: Introduction to MLOps
 
Consolidating MLOps at One of Europe’s Biggest Airports
Consolidating MLOps at One of Europe’s Biggest AirportsConsolidating MLOps at One of Europe’s Biggest Airports
Consolidating MLOps at One of Europe’s Biggest Airports
 

More from Databricks

Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized PlatformDemocratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
Databricks
 
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI IntegrationStage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Databricks
 
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchSimplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Databricks
 
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction QueriesRaven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
Databricks
 

More from Databricks (20)

DW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptxDW Migration Webinar-March 2022.pptx
DW Migration Webinar-March 2022.pptx
 
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Data 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 2Data Lakehouse Symposium | Day 1 | Part 2
Data Lakehouse Symposium | Day 1 | Part 2
 
Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2Data Lakehouse Symposium | Day 2
Data Lakehouse Symposium | Day 2
 
Data Lakehouse Symposium | Day 4
Data Lakehouse Symposium | Day 4Data 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 Hadoop5 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 PlatformDemocratizing 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 ScienceLearn 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 MonitoringWhy 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 FixThe 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 IntegrationStage 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 PyTorchSimplify 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 KubernetesScaling 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 PipelinesScaling 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 AggregationsSawtooth 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 SinkRedis + 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 SparkRe-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 QueriesRaven: 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 SparkProcessing 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 LakeMassive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta Lake
 

Recently uploaded

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
amitlee9823
 
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escor...
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men  🔝Thrissur🔝   Escor...➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men  🔝Thrissur🔝   Escor...
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escor...
amitlee9823
 
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men  🔝mahisagar🔝   Esc...➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men  🔝mahisagar🔝   Esc...
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...
amitlee9823
 
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night StandCall Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
amitlee9823
 
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men  🔝Dindigul🔝   Escor...➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men  🔝Dindigul🔝   Escor...
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...
amitlee9823
 
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...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
amitlee9823
 
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night StandCall Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
amitlee9823
 
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
amitlee9823
 
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts ServiceCall Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men  🔝malwa🔝   Escorts Ser...➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men  🔝malwa🔝   Escorts Ser...
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...
amitlee9823
 
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
amitlee9823
 
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
amitlee9823
 
CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Rabindra Nagar  (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICECHEAP Call Girls in Rabindra Nagar  (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night StandCall Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
amitlee9823
 

Recently uploaded (20)

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24  Building Real-Time Pipelines With FLaNKDATA SUMMIT 24  Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
 
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
 
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escor...
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men  🔝Thrissur🔝   Escor...➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men  🔝Thrissur🔝   Escor...
➥🔝 7737669865 🔝▻ Thrissur Call-girls in Women Seeking Men 🔝Thrissur🔝 Escor...
 
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men  🔝mahisagar🔝   Esc...➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men  🔝mahisagar🔝   Esc...
➥🔝 7737669865 🔝▻ mahisagar Call-girls in Women Seeking Men 🔝mahisagar🔝 Esc...
 
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night StandCall Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Doddaballapur Road ☎ 7737669865 🥵 Book Your One night Stand
 
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
 
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men  🔝Dindigul🔝   Escor...➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men  🔝Dindigul🔝   Escor...
➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...
 
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...
Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night StandCall Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand
 
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...
 
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts ServiceCall Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service
 
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men  🔝malwa🔝   Escorts Ser...➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men  🔝malwa🔝   Escorts Ser...
➥🔝 7737669865 🔝▻ malwa Call-girls in Women Seeking Men 🔝malwa🔝 Escorts Ser...
 
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceBDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
 
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...
 
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...
 
Discover Why Less is More in B2B Research
Discover Why Less is More in B2B ResearchDiscover Why Less is More in B2B Research
Discover Why Less is More in B2B Research
 
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
 
CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Rabindra Nagar  (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICECHEAP Call Girls in Rabindra Nagar  (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
CHEAP Call Girls in Rabindra Nagar (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE
 
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night StandCall Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand
 

Building a MLOps Platform Around MLflow to Enable Model Productionalization in Just a Few Minutes

  • 1. Building a MLOps platform around mlflow to enable model productionalization in just a few minutes Milan Berka ML Engineer @ DataSentics
  • 2. Agenda I. Few words about DataSentics II. Motivation - common data analytical problems we encounter III. MLOps – how do we understand it, what it means for getting data scientist’s model into production within our platform IV. Demo V. Lessons learned and next steps
  • 3. Few words about us Agile Data Analytics platform Make data science and machine learning have a real impact on organizations across the world - demystify the hype and black magic surrounding AI/ML and bring to life transparent production-level data science solutions and products delivering tangible impact and innovation. European Data Science Center of Excellence based in Prague ▪ Machine learning and cloud data engineering boutique ▪ Helping customers build end-to-end data solutions in cloud ▪ Incubator of ML-based products ▪ 80 specialist (data science, data/software engineering) ▪ Partner of Databricks & Microsoft
  • 4. Problems we commonly encounter At our own data-driven products and at our customers: To combat these problems, we assembled a dedicated team: TechSentics, whose goal is to systematically scale the technical quality of data driven products and deliver ML systems which tackle those problems in context of enterprise security and other processes. We want a small agile team of data scientists and engineers to be able to prototype new advanced analytics use cases using diverse internal and external data in weeks and bring them into production in a few months We need high quality data for our model development, and we need to have access to the data! We want the feature store! We don’t want to buy magical AI black box solutions. We want our own open platform We want to focus on the use cases and logic, not the DevOps and plumbing We put together a team of data scientists, they create a ML model, everyone tells how performant it is, but despite this, it never gets beyond the experimentation phase and never really affects anything. As data scientists, we feel that software engineers and software architects sometimes do not understand us! Data scientists need real production data for the experimentation and model development. Model != code, need different skills to make, different tests to apply! Sometimes it takes hours to get a reasonable model, sometimes it takes weeks or more! There is a rich cloud-based service offering that claim to address the problems, but there is always some stitching required to make it work.
  • 5. Data intensive tooling by TechSentics Cloud Advanced Data Analytics Platform Feature Store and MLOps Isolated environments with DevOps Data lake house tooling & governance Utilizing Databricks and Azure with portability to different spark ecosystem Managing the lifecycle of a model and features Model & Data & Code as microservices Unique hybrid delivery mode for large enterprises Enterprise-ready and automated data analytics in azure, proven at banks. Open-source soon. Our flagship to help ML to play a significant role in organizations and keep high SLAs
  • 6. Our take on MLOps Set of practices for managing and streamlining machine learning (ML) models' lifecycle - from development all the way to production. MLOps topics: ▪ Feature Store - one central place for storing and managing ML features across the company ▪ Model Registery - place where to store model artifacts and other necessary files ▪ Model deployment - process of deploying model ▪ Model operations - monitoring / logging / retraining / optimizing the model application ▪ Model reproducibility & portability & interpretability (explainability) ▪ XAI in MLOps (Explain Ops) ▪ Standardization & reusability - template / interface (API, SDK..) for data scientists to ease the development and deployment
  • 7. Our take on MLOps
  • 9. Overview Model Development Assistant: ▪ Provides templates how to structure and develop machine learning projects - to comply with the rest of the MLOps module frame. Model Repository (MLflow framework): ▪ Experimental tracking of metadata (metrics, parameters, tags, code) and artifacts (models, data) for the data science model training experiments / trials. Serving Manager: ▪ Catalog of prepared templates for deployment of the ML models. By default supports Azure Databricks Spark/Python/R batch/streaming jobs, Docker packaging, deployment of real-time endpoints on Azure Container Instances; extendable ▪ Management of retraining and release pipelines. ▪ Automatic pipeline triggers: “model stage transition”-based trigger, “model performance”- based trigger, time-based trigger. Support for custom triggers. Monitoring and alerting: ▪ Model drift and skew monitoring ▪ Alerting if metrics exceeds a given threshold
  • 10. Lessons Learned & Next steps Lessons learned • MLOps is not easy to adopt, requires a lot of involvement and is still rather open topic • mlflow in its open source form is quite naked and there is a lot to be developed. Especially surprising is the artifact store “bypass” • Data Scientists are not always super-used to some common software practices such as git • Writing ML tests can be tricky • Models can be trained directly in Automation tools ☺ • There is a lot of interest from both internal and external teams => we shipped the platform to our customers as well Next steps for our platform: - One centralized MLOps module interface & front-end, instead of multiple interfaces for each component (replacing the need to interact with the Azure DevOps (or other similar provider), mlflow, ACI, Azure Monitor front-ends for certain parts of the workflow). - Introduce ExplainOps component, providing explanations for obtained predictions. - Richer deployment offering (canary deployments, wider target environment type support) - Tighter feature store integration
  • 11. Call to action As mentioned, because of the high interest, we are about to… … open-source the infrastructural parts of the platform! Let’s push this forward together! We are looking for first 2-3 companies willing to use it in challenging environment & contribute.
  • 12. Platform Team - kudos Jan ProchazkaTomas Kresal Anastasia Lebedeva Jiri Koutny Michal Halenka Marek Lipan Nikola Valesova Michal Mlaka Oldrich Vlasic Ondrej Havlicek Jan Perina Samuel Messa
  • 14. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.