14–17 Oct 2019

Training and deploying Python models on Azure

Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
9:0012:30 Tuesday, 15 October 2019
Location: Windsor Suite
Average rating: ****.
(4.33, 6 ratings)

Who is this presentation for?

  • Anyone aiming to train and deploy Python models




Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline material from the newly released GitHub repository for recommended ways to train and deploy models on Azure, ranging from from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. They take you step-by-step through the process to train or deploy your model using Jupyter notebooks for Python.

You’ll get your hands dirty with code on three scenarios with code made available on GitHub.


  • Train LightGBM model locally and run hyperparameter tuning using Hyperdrive, deploy on Kubernetes for real-time scoring
  • Deploy PyTorch-style transfer model for batch scoring using Azure ML pipelines
  • Distributed training of ResNet50 model using Batch AI, deploy a Python image classification model on Kubernetes for real-time scoring

Prerequisite knowledge

  • General knowledge of Python
  • A basic understanding of Docker (useful but not required)

Materials or downloads needed in advance

  • A WiFi-enabled laptop (You'll receive an access code for free Azure usage.)

What you'll learn

  • Learn recommended ways to train and deploy Python models on Azure
Photo of Danielle Dean

Danielle Dean


Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.

Photo of Mathew Salvaris

Mathew Salvaris


Mathew Salvaris is a data scientist at Microsoft. Previously, Mathew was a data scientist for a small startup that provided analytics for fund managers; a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will, devising models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and a postdoc in the University of Essex’s Brain Computer Interface Group, where he worked on BCIs for computer mouse control. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.

Photo of Wee Hyong Tok

Wee Hyong Tok


Wee Hyong Tok is a principal data science manager with the AI CTO Office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.

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