Building Machine Learning Models Automatically (June 2020)
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SageMaker AutoPilot AutoGluon
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Building Machine Learning Models Automatically (June 2020)
- 1. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Building machine learning models automatically Julien Simon Global AI/ML Evangelist, AWS @julsimon
- 2. AutoML • AutoML aims at automating the process of building a model • Problem identification: looking at the data set, what class of problem are we trying to solve? • Algorithm selection: which algorithm is best suited to solve the problem? • Data preparation: how should data be prepared for best results? • Hyperparameter tuning: what is the optimal set of training parameters?
- 3. Scenarios for AutoML • Build models without any ML expertise • Empower more people in your organization: software developers, business people • Let experts focus on hard problems • Experiment and build models at scale • Thousands of data sets can be modeled without human intervention • Let experts focus on new problems • Automate 90% of the work, then tweak • Data cleaning, feature engineering, feature selection, etc. • Let experts focus on high value tasks such domain knowledge, and error analysis.
- 4. Transparency and control are important • Get the best model only • Hard to understand it • Hard to reproduce it manually • Get the best model, all candidates, full source code • Understand how the model was built • Keep tweaking for extra performance
- 5. Agenda • Zero-code AutoML with SageMaker Studio • AutoML with SageMaker AutoPilot and the SageMaker SDK • Open source AutoML with AutoGluon
- 6. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 7. Amazon SageMaker helps you build, train, and deploy models Fully managed data processing jobs and data labeling workflows Prepare 101011010 010101010 000011110 Collect and prepare training data Build One-click collaborative notebooks and built-in, high performance algorithms and models Choose or build an ML algorithm Web-based IDE for machine learning Automatically build and train models Train & Tune One-click training Debugging and optimization Visually track and compare experiments Set up and manage environments for training Train, debug, and tune models Manage training runs Deploy & Manage One-click deployment and auto scaling Automatically spot concept drift Fully managed with auto-scaling for 75% less Deploy model in production Monitor models Add human review of predictions Modular service and APIs, from experimentation to production
- 8. AutoML with Amazon SageMaker Autopilot • SageMaker Autopilot covers all steps • Problem identification: looking at the data set, what class of problem are we trying to solve? • Algorithm selection: which algorithm is best suited to solve the problem? • Data preprocessing: how should data be prepared for best results? • Hyperparameter tuning: what is the optimal set of training parameters? • Autopilot is fully transparent • An auto-generated notebook shows you how models are built • You can run it, reproduce the experiment, and keep tweaking • Use cases: regression and classification, based on Linear Learner, Factorization Machines, KNN, XGBoost
- 9. AutoML with Amazon SageMaker Autopilot 1. Upload the unprocessed dataset to S3 2. Configure the AutoML job • Location of dataset • Target attribute • Completion criteria 3. Launch the job 4. View the list of candidates and the autogenerated notebook 5. Deploy the best candidate to a real-time endpoint, or use batch transform
- 10. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 11. Apache MXNet • Open source software library for Deep Learning • Natively implemented in C++ • Built-in support for many network architectures: FC, CNN, LSTM, etc. • Symbolic API: Python, Scala, Clojure, R, Julia, Perl, Java (inference only) • Imperative API: Gluon (Python), with computer vision, natural language processing, and time series toolkits
- 12. AutoGluon https://autogluon.mxnet.io/ • Open source AutoML toolkit • Tabular data: regression, classification • LightGBM, CatBoost, Random Forests, Extra Trees, KNN, linear regression • Text data: classification • Transfer learning based on Gluon NLP models • Image data: classification, object detection • Transfer learning based on Gluon CV models • Built-in hyperparameter optimization • Built-in ensemble prediction (bagging & stacking)
- 13. from autogluon import TabularPrediction as task predictor = task.fit(train_data=task.Dataset( file_path=TRAIN_DATA.csv), label=COLUMN_NAME) predictions = predictor.predict( task.Dataset(file_path=TEST_DATA.csv)) AutoML on tabular data in 3 lines of code
- 14. Demo: AutoGluon on the Boston Housing Dataset https://gitlab.com/juliensimon/dlnotebooks/- /tree/master/autogluon
Editor's Notes
- Amazon SageMaker is a fully managed service that removes the heavy lifting from each step of the machine learning process. Since launch, AWS has regularly added new capabilities to Amazon SageMaker, with more than 50 new capabilities delivered over the past 12 months. These capabilities have helped many more developers build custom machine learning models. But just as barriers to machine learning adoption have been removed by Amazon SageMaker, customer desire to utilize machine learning at scale has only increased. Amazon SageMaker makes a lot of the steps required to develop great machine learning models much easier. Amazon SageMaker brings lots of tools together through an IDE so you can test, debug, deployment, monitoring, and profile all within a single environment. With Amazon SageMaker, you have the flexibility to use automated machine learning or use any component as a modular capability.