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Automate your Machine Learning

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Ajit Ananthram

A demonstration of AutoML in Azure Machine Learning

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Automate your Machine Learning Ajit Ananthram Principal Consultant, FTS Data & AI Microsoft Data Wranglers Meetup - Dec 2019 (Sydney)
Machine Learning Lifecycle Data pre-processing ML algorithm(s) selection Training Testing Tuning Hosting
ML Algorithm Selection
Traditional ML - Challenges Repetitive & time- consuming Resource-intensive Requires deep statistical knowledge
Automated Machine Learning (AutoML) Automation of time- consuming processes Incorporation of best practices Democratisation of machine learning
Demo – Titanic Dataset Objective: Predict if a passenger survived Titanic’s maiden voyage based on known parameters Reference: https://www.kaggle.com/c/titanic/ Feature Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton
Data Pre-processing • SQL • Python • R • Etc. Languages • ‘Unknown’ added for missing values • Age converted to a range • Fare converted to a range Steps taken in Titanic Dataset
Automated Machine Learning in Microsoft Azure Demo
Iterations • Runs consist of multiple combinations of ML algorithms & hyperparameters • Stops when exit criteria reached • Deploy best model as web service
Measurements Metric Purpose Equation Accuracy Indication of success-rate for true positives & true negatives (Tp + Tn) / (Tp + Tn + Fp + Fn) Precision Indication of success-rate for true positives Tp / (Tp + Fp) Recall Indication of impact of false negatives Tp / (Tp + Fn) (Tn) (Fp) (Tp)(Fn)
Interpretability • ‘Explainability’ of ML output critical for success • Explain globally (all data) or locally (specific point) • Run on compute target
Hosting • Test in an Azure Container Instance (ACI) container • Productionise using an Azure Kubernetes Services (AKS) cluster • Consume REST web-service in applications
Drift Detection • Data drift causes model performance degradation • Detect drift on timeseries data • Set-up alerts in Application Insights Reference - https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-monitor-datasets
AutoML – Code-based Experience
MLOps Reference - https://github.com/Microsoft/MLOps • Integrate Azure Machine Learning with Azure DevOps (AML extension) • Enable collaboration between Data Scientists and other teams • Set-up CI/CD pipelines using an effecting branching strategy
Conclusions • AutoML brings data science activities to a wider audience • It reduces repetitive processing time thereby freeing up time for higher-level activities • Microsoft Azure Machine Learning is constantly evolving

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Automate your Machine Learning

  • 1. Automate your Machine Learning Ajit Ananthram Principal Consultant, FTS Data & AI Microsoft Data Wranglers Meetup - Dec 2019 (Sydney)
  • 2. Machine Learning Lifecycle Data pre-processing ML algorithm(s) selection Training Testing Tuning Hosting
  • 4. Traditional ML - Challenges Repetitive & time- consuming Resource-intensive Requires deep statistical knowledge
  • 5. Automated Machine Learning (AutoML) Automation of time- consuming processes Incorporation of best practices Democratisation of machine learning
  • 6. Demo – Titanic Dataset Objective: Predict if a passenger survived Titanic’s maiden voyage based on known parameters Reference: https://www.kaggle.com/c/titanic/ Feature Definition Key survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton
  • 7. Data Pre-processing • SQL • Python • R • Etc. Languages • ‘Unknown’ added for missing values • Age converted to a range • Fare converted to a range Steps taken in Titanic Dataset
  • 8. Automated Machine Learning in Microsoft Azure Demo
  • 9. Iterations • Runs consist of multiple combinations of ML algorithms & hyperparameters • Stops when exit criteria reached • Deploy best model as web service
  • 10. Measurements Metric Purpose Equation Accuracy Indication of success-rate for true positives & true negatives (Tp + Tn) / (Tp + Tn + Fp + Fn) Precision Indication of success-rate for true positives Tp / (Tp + Fp) Recall Indication of impact of false negatives Tp / (Tp + Fn) (Tn) (Fp) (Tp)(Fn)
  • 11. Interpretability • ‘Explainability’ of ML output critical for success • Explain globally (all data) or locally (specific point) • Run on compute target
  • 12. Hosting • Test in an Azure Container Instance (ACI) container • Productionise using an Azure Kubernetes Services (AKS) cluster • Consume REST web-service in applications
  • 13. Drift Detection • Data drift causes model performance degradation • Detect drift on timeseries data • Set-up alerts in Application Insights Reference - https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-monitor-datasets
  • 15. MLOps Reference - https://github.com/Microsoft/MLOps • Integrate Azure Machine Learning with Azure DevOps (AML extension) • Enable collaboration between Data Scientists and other teams • Set-up CI/CD pipelines using an effecting branching strategy
  • 16. Conclusions • AutoML brings data science activities to a wider audience • It reduces repetitive processing time thereby freeing up time for higher-level activities • Microsoft Azure Machine Learning is constantly evolving

Notes de l'éditeur

  1. Intro Poll Objectives
  2. Run 21
  3. Dataset monitors
  4. https://ml.azure.com/fileexplorer?wsid=/subscriptions/b5f23f19-6ff1-41aa-9846-de40165003d7/resourcegroups/MSMeetup_Ajit_rg/workspaces/aml_ws&activeFilePath=Users/Ajit.Ananthram/tutorials/regression-automated-ml.ipynb