201906 02 Introduction to AutoML with ML.NET 1.0
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ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more. “Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019. This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
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201906 02 Introduction to AutoML with ML.NET 1.0
- 1. Data and AI Scientist @ Microsoft Cloud Solution Architect US CTO Customer Success @marktabnet
- 3. ML.NET Open Source Momentum 150K+ 1,427 1,528 106
- 4. ML.NET Customers Andy Gray, Executive Partner Evolution Software Design, Inc.
- 7. DESKTOP CLOUDWEB MOBILE ML .NET Your platform for building anything IoTGAMING
- 8. “It has exquisite buttons … with long sleeves …works for casual as well as business settings”{f(x) {f(x) Machine Learning “Programming the UnProgrammable”
- 9. f(x) Model Machine Learning creates a using this data Machine Learning “Programming the UnProgrammable”
- 10. ML.NET 1.0 Machine Learning framework for building custom ML Models Custom ML made easy Automated ML and Tools (Model Builder and CLI) Proven at scale Azure, Office, Windows Extensible TensorFlow, ONNX and Infer.NET Cross-platform and open-source Runs everywhere
- 12. Building blocks for a Data Science Project Data sources
- 13. 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.
- 14. 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
- 15. How much is this car worth? Machine Learning Problem Example
- 16. 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
- 17. 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
- 18. Which algorithm? Which parameters?Which features? Iterate Model Creation Is Typically Time-Consuming
- 19. Enter data Define goals Apply constraints Output Automated ML Accelerates Model Development Input Intelligently test multiple models in parallel Optimized model
- 20. 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
- 21. 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
- 22. Data Preprocessing Feature Engineering Algorithm Selection Hyper-parameter Tuning Model Recommendation Interpretability & Explaining 1. 2. 3. 4. 5. 6. © Microsoft Corporation Azure Automated ML
- 23. 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
- 24. What’s new?
- 25. 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
- 26. ML.NET AutoML
- 27. Automated Machine Learning (AutoML)
- 28. On the command line, with the ML.NET CLI mlnet auto-train --task binary-classification --dataset "yelp_labelled.txt" --label-column-index 1 --has-header false --max-exploration-time 10
- 29. With a graphical user interface, with the the ML.NET Model Builder
- 30. https://github.com/dotnet/machinelearning- samples/tree/master/samples/csharp/getting- started/BinaryClassification_AutoML Via an application, with the automated ML API
- 31. Automated ML Customer Testimonials • Press-coverage from public preview: • CNET • VentureBeat • PRNewswire “I quite like your AutoML function. It gives me good results compared to other libraries I tested before (tpot and auto-sklearn) that I believe was only looking at scores and often gave me models that over-trained my data. And of course the model from your suggested code is better.” - Big oil company “I will start with AutoML and use the algorithm that AutoML recommends to further tune the model” - Data Scientist “I actually enjoy being able to use AutoML in a Jupyter notebook. The DataRobot interface was nice for non-experts, but for someone like me, it felt a bit basic.” - Data Scientist