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.

Datos y análisis

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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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

Databricks

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

Databricks

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

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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

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

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

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