201908 Overview of Automated ML
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Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
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201908 Overview of Automated ML
- 1. Data and AI Scientist @ Microsoft Cloud Solution Architect US CTO Customer Success @marktabnet
- 2. © Microsoft Corporation Agenda • Why Automated Machine Learning? • Azure ML Service and Azure Databricks • Capabilities: What’s New? • Demos • Enterprise Deployment
- 3. Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
- 6. Machine Learning on Azure Domain Specific Pretrained Models To reduce time to market Azure Databricks Machine Learning VMs Popular Frameworks To build machine learning and deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive Services To empower data science and development teams Powerful Hardware To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science Tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
- 7. Building blocks for a Data Science Project Data sources
- 8. What is automated machine learning? © Microsoft Corporation Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data.
- 9. Automated ML Mission Democratize AI Scale AIAccelerate AI © Microsoft Corporation Azure Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI Enable Domain Experts & Developers to get rapidly build AI solutions Improve Productivity for Data Scientists, Citizen Data Scientists, App Developers & Analysts Build AI solutions at scale in an automated fashion
- 10. How much is this car worth? Machine Learning Problem Example
- 11. Model Creation Is Typically Time-Consuming Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf Others Model Which algorithm? Which parameters?Which features? Car brand Year of make
- 12. Criterion Loss Min Samples Split Min Samples Leaf Others N Neighbors Weights Metric P Others Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Nearest Neighbors Model Iterate Gradient BoostedMileage Car brand Year of make Car brand Year of make Condition Model Creation Is Typically Time-Consuming
- 13. Which algorithm? Which parameters?Which features? Iterate Model Creation Is Typically Time-Consuming
- 14. Enter data Define goals Apply constraints Output Automated ML Accelerates Model Development Input Intelligently test multiple models in parallel Optimized model
- 15. Automated ML Capabilities • Based on Microsoft Research • Brain trained with several million experiments • Collaborative filtering and Bayesian optimization • Privacy preserving: No need to “see” the data
- 16. Automated ML Capabilities • ML Scenarios: Classification & Regression, Forecasting • Languages: Python SDK for deployment and hosting for inference – Jupyter notebooks • Training Compute: Local Machine, AML Compute, Data Science Virtual Machine (DSVM), Azure Databricks* • Transparency: View run history, model metrics, explainability* • Scale: Faster model training using multiple cores and parallel experiments * In Preview
- 17. Guardrails Class imbalance Train-Test split, CV, rolling CV Missing value imputation Detect high cardinality features Detect leaky features Detect overfitting Model Interpretability / Feature Importance
- 18. About Azure ML Service and Azure Databricks
- 19. + To empower data science and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
- 20. Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Tool agnostic Python SDK Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere
- 21. Fast, easy, and collaborative Apache Spark™-based analytics platform Built with your needs in mind Optimized Apache Spark environmnet Collaborative workspace Integration with Azure data services Autoscale and autoterminate Optimized for distributed processing Support for multiple languages and libraries Seamlessly integrated with the Azure Portfolio Increase productivity Build on a secure, trusted cloud Scale without limits
- 22. Leverage your favorite deep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
- 23. What to use when? + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
- 24. What’s new?
- 25. Latest announcements @ MS Build (Blog post with all the announcements) Feature engineering updates • Additional data guardrails and synthetic features • Added XGBoost algorithm • Improved transparency retrieving the engineered features © Microsoft Corporation Azure Coming up next • Improved feature sweeping, text featurization • Transparency: Get auto-featurized data
- 26. Latest announcements @ MS Build (Blog post with all the announcements) Time Series Forecasting Generally Available • Rolling cross validation splits for time series data • Configurable lags • Window aggregation • Holiday featurizer © Microsoft Corporation Azure https://azure.microsoft.com/ en-us/blog/build-more- accurate-forecasts-with- new-capabilities-in- automated-machine- learning/
- 27. Latest announcements @ MS Build (Blog post with all the announcements) Automated ML in ML.NET Model Builder (Preview) • Train ML models from Visual Studio • Inference from your application © Microsoft Corporation Azure ML.NET Model Builder
- 28. Latest announcements @ MS Build (Blog post with all the announcements) ONNX support • Automated ML output ONNX format models • Inferencing support for C# apps via ONNX runtime environments (WinML, ML.Net, ONNX C# API), Cosmos pipelines © Microsoft Corporation Azure
- 29. Latest announcements @ MS Build (Blog post with all the announcements) Run automated ML from SQL © Microsoft Corporation Azure Blog post
- 30. Latest announcements @ MS Build (Blog post with all the announcements) Automated ML UI in Azure portal (Preview) • End-to-end no-code experience for non-data scientists to train ML models • Classification, Regression, Forecasting • Deploy models easily and quickly • Advanced settings for power users to tune the training job © Microsoft Corporation Azure Blog post Coming up next • Model explainability • Additional data sources (with Datasets) • Re-run experiments
- 31. Demo: Azure Machine Learning Service
- 32. https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-portal-experiments
- 34. About Azure Databricks • Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. • It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
- 35. Azure Databricks Features • Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning. • You may keep the data within the same cluster. • You may leverage the local worker nodes with autoscale and auto termination capabilities. • You may use multiple cores of your Azure Databricks cluster to perform simultaneous training. • You may further tune the model generated by automated machine learning. • Every run (including the best run) is available as a pipeline, which you may tune further if needed. • The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
- 37. How to Configure Azure Databricks https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks
- 39. Deploy Azure ML models at scale Azure Machine Learning Service
- 40. Model deployment
- 42. Action
- 43. Try it for free http://aka.ms/amlfree Learn more : https://aka.ms/automatedmldocs Notebook Samples : https://aka.ms/automatedmlsamples Blog Post : https://aka.ms/AutomatedML Product Feedback : AskAutomatedML@microsoft.com