Introducing MLflow for End-to-End Machine Learning on Databricks

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Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12.

Data & Analytics

Introducing MLﬂow for
End-to-End Machine Learning
on Databricks
Sean Owen
Principal Solutions Architect @ Databricks

A Data Science Lifecycle in Many Steps
▪ Enable access to data
▪ Secure data
▪ Clean and standardize
data
▪ Make it discoverable
▪ Make it reliable
▪ Join and aggregate
relevant data
▪ Explore and visualize it
▪ Experiment with
modeling techniques
▪ Tune and select a best
model
▪ Translate model to
production-ready form
▪ Execute inference
▪ Monitor modeling jobs
▪ Operate data pipelines
Data Science ProductionizationData Engineering

Data Science and ML on
AutoML
End-to-End ML Lifecycle
ML Runtime and
Environments
Batch
Scoring
Online
Serving
Data Science Workspace
Prep Data Build Model Deploy/Monitor Model
Open,pluggable
architecture

Demo:
Predicting Life Expectancy
Updated!

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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

Databricks

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

Databricks

Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models. In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.

Machine Learning CI/CD for Email Attack Detection

Databricks

Sarah: CEO-Finance-Report pipeline seems to be slow today. Why Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days. Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours. Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert. We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example: Tom: I am not seeing any data for today in my Campaign Metrics Dashboard. Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021. This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Databricks

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Jeeves Grows Up: An AI Chatbot for Performance and Quality

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Introducing MLflow for End-to-End Machine Learning on Databricks

1. Introducing MLﬂow for End-to-End Machine Learning on Databricks Sean Owen Principal Solutions Architect @ Databricks

2. A Data Science Lifecycle in Many Steps ▪ Enable access to data ▪ Secure data ▪ Clean and standardize data ▪ Make it discoverable ▪ Make it reliable ▪ Join and aggregate relevant data ▪ Explore and visualize it ▪ Experiment with modeling techniques ▪ Tune and select a best model ▪ Translate model to production-ready form ▪ Execute inference ▪ Monitor modeling jobs ▪ Operate data pipelines Data Science ProductionizationData Engineering

3. Data Science and ML on AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open,pluggable architecture

4. Demo: Predicting Life Expectancy Updated!

5. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

Introducing MLflow for End-to-End Machine Learning on Databricks

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