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Accelerating Data Science and Machine Learning Workflow with Azure Machine Learning

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Aditya Bhattacharya
Aditya Bhattacharya

Accelerating Data Science and Machine Learning Workflow with Microsoft Azure Machine Learning Microsoft User Group Hyderabad AIML Day 2020 https://aditya-bhattacharya.net/ https://www.eventbrite.com/e/microsoft-user-group-hyderabad-aiml-day-2020-tickets-123940376001 MUGH

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Accelerating Data Science and Machine Learning workflow with Azure Machine Learning ~ Aditya Bhattacharya Lead AI/ML Engineer, West Pharmaceuticals Services
About Me Currently, I am leading the Data Science team at West Pharmaceutical Services and previously worked in Microsoft and well seasoned in domains such as Machine Learning, Deep Learning, Internet of Things (IoT), Robotics and Cloud Computing. In the Data Science domain, I am well experienced with Computer Vision, Time-Series Forecasting, NLP, Speech analysis. Apart from my day job, I am an AI Researcher at an NGO called MUST Research, and I am one of the faculty members at MUST Research Academy. https://academy.must.co.in/ https://aditya-bhattacharya.net/ https://www.linkedin.com/in/aditya-bhattacharya-b59155b6/
Key topics for discussion 1. Enterprise Data Science and Machine Learning workflow 2. AzureML studio for 360○ workspace for accelerating ML solutions from research to production 3. AzureML Python SDK for building custom ML pipelines 4. Smooth integration from other services in Azure for a scalable ML solution
Industry 4.0 Expectations • Data driven workflows and smart systems powered by AI and Machine Learning • Fill the gap between advance research and leveraging benefits of technology at scale • Minimize human intervention and human bias and improve throughput using Automated AI/ML solutions.
How does a scalable and sustainable ML workflow look like?
Typical Enterprise MLWork Flow User Interface Layer Middleware API Layer Analytics LayerData Layer Monitoring Layer Feedback Layer
Typical challenges for Enterprise Data Scientists for production level solutions • Creating data pipelines for continuous flow of data • Infrastructure needs -> need of flexible computing infrastructure to deal with large volume of data • Rapid prototyping ofAnalytical and Machine Learning models • Model versioning and comparison • Model monitoring and re-training • Exposing model results as REST API endpoints for integrating with other software applications
360○ Solution from Azure ML Studio • Integration with Jupyter Notebooks, AutoML and designer view for rapid prototyping of ML models and DS Solutions. • Data registry, Model registry, Model deployment as a Web service for accelerating from research to production. • Managing computes, data stores and data manipulation tools under one umbrella.
Azure ML Studio Walkthrough
Custom ML Pipelines with AzureML SDK
Two types of Production DS Workflow Online Model Flow Offline Model Flow
Offline Model Flow • OfflineTraining – Live Deployment • For static data used for training – when the data is not dynamic and unknown to the training model • Model Registering andVersioning through Azure ML • Model Deployment Pipeline and result can be consumed through RESTAPI for real time inference Offline Training Deploying trained model using Azure ML Managed by user application
Online Model Flow • Online training – Live Deployment • For dynamic data used for training – when the end user can configure the data to be used for training the model – user configuration driven data • Deployment on unknown samples and can be consumed in real-time • Can be auto-tuned and can run without specialized attention by the Data Science team Managed by user application Online training and deployment pipeline using Azure ML Managed by user application
User Application REST API Layer Custom Azure ML Ops Pipeline UsingAzure ML SDK
Integration with Other Azure Services
Link - https://docs.microsoft.com/en-us/azure/machine-learning/overview-what-is- azure-ml#integration-with-other-services
Summary • Enterprise Data Science and Machine Learning workflow • AzureML studio for 360○ workspace for accelerating ML solutions from research to production • Building custom ML pipelines using Azure ML SDK • Easy integration from other services in Azure for a scalable ML solution
- ADITYA BHATTACHARYA Questions? -Want to connect over LinkedIn ? - Or email me at: aditya.bhattacharya2016@gmail.com - Follow me at: https://aditya-bhattacharya.net/ That’s all for today!

More Related Content

Accelerating Data Science and Machine Learning Workflow with Azure Machine Learning

  • 1. Accelerating Data Science and Machine Learning workflow with Azure Machine Learning ~ Aditya Bhattacharya Lead AI/ML Engineer, West Pharmaceuticals Services
  • 2. About Me Currently, I am leading the Data Science team at West Pharmaceutical Services and previously worked in Microsoft and well seasoned in domains such as Machine Learning, Deep Learning, Internet of Things (IoT), Robotics and Cloud Computing. In the Data Science domain, I am well experienced with Computer Vision, Time-Series Forecasting, NLP, Speech analysis. Apart from my day job, I am an AI Researcher at an NGO called MUST Research, and I am one of the faculty members at MUST Research Academy. https://academy.must.co.in/ https://aditya-bhattacharya.net/ https://www.linkedin.com/in/aditya-bhattacharya-b59155b6/
  • 3. Key topics for discussion 1. Enterprise Data Science and Machine Learning workflow 2. AzureML studio for 360○ workspace for accelerating ML solutions from research to production 3. AzureML Python SDK for building custom ML pipelines 4. Smooth integration from other services in Azure for a scalable ML solution
  • 4. Industry 4.0 Expectations • Data driven workflows and smart systems powered by AI and Machine Learning • Fill the gap between advance research and leveraging benefits of technology at scale • Minimize human intervention and human bias and improve throughput using Automated AI/ML solutions.
  • 5. How does a scalable and sustainable ML workflow look like?
  • 6. Typical Enterprise MLWork Flow User Interface Layer Middleware API Layer Analytics LayerData Layer Monitoring Layer Feedback Layer
  • 7. Typical challenges for Enterprise Data Scientists for production level solutions • Creating data pipelines for continuous flow of data • Infrastructure needs -> need of flexible computing infrastructure to deal with large volume of data • Rapid prototyping ofAnalytical and Machine Learning models • Model versioning and comparison • Model monitoring and re-training • Exposing model results as REST API endpoints for integrating with other software applications
  • 8. 360○ Solution from Azure ML Studio • Integration with Jupyter Notebooks, AutoML and designer view for rapid prototyping of ML models and DS Solutions. • Data registry, Model registry, Model deployment as a Web service for accelerating from research to production. • Managing computes, data stores and data manipulation tools under one umbrella.
  • 12. Offline Model Flow • OfflineTraining – Live Deployment • For static data used for training – when the data is not dynamic and unknown to the training model • Model Registering andVersioning through Azure ML • Model Deployment Pipeline and result can be consumed through RESTAPI for real time inference Offline Training Deploying trained model using Azure ML Managed by user application
  • 13. Online Model Flow • Online training – Live Deployment • For dynamic data used for training – when the end user can configure the data to be used for training the model – user configuration driven data • Deployment on unknown samples and can be consumed in real-time • Can be auto-tuned and can run without specialized attention by the Data Science team Managed by user application Online training and deployment pipeline using Azure ML Managed by user application
  • 17. Summary • Enterprise Data Science and Machine Learning workflow • AzureML studio for 360○ workspace for accelerating ML solutions from research to production • Building custom ML pipelines using Azure ML SDK • Easy integration from other services in Azure for a scalable ML solution
  • 18. - ADITYA BHATTACHARYA Questions? -Want to connect over LinkedIn ? - Or email me at: aditya.bhattacharya2016@gmail.com - Follow me at: https://aditya-bhattacharya.net/ That’s all for today!