14–17 Oct 2019

Azure AI reference architectures

Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Average rating: ****.
(4.00, 2 ratings)

Who is this presentation for?

  • Anyone wishing to train and deploy models on Azure




Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Each tutorial in the repo takes you step by step through the process to train or deploy your model. And they’re set up as Jupyter notebooks (for Python) and R Markdown (for R), so you can download them and start running them whenever you want.

Join in to learn lessons distilled from working with large global customers on AI and ML projects and the challenges that were overcome. Although Danielle, Wee Hyong, and Mathew can only cover a couple topics in depth, you’ll discover how to deploy a Python image classification model on Kubernetes for real-time scoring; train a LightGBM model locally using Azure Machine Learning and deploy on Kubernetes for real-time scoring; train a LightGBM model locally and run hyperparameter tuning using Hyperdrive; deploy a PyTorch-style transfer model for batch scoring using Azure ML pipelines; deploy a one-class SVM for batch scoring anomaly detection using Azure ML pipelines; deploy an ML model for real-time scoring on Kubernetes; deploy a forecasting model for batch scoring using Azure Batch and doAzureParallel; deploy a one-class SVM for batch scoring anomaly detection using Azure ML pipelines; and use Batch AI for distributed training of a ResNet50 model.

Code samples will be available on GitHub.

Prerequisite knowledge

  • A working knowledge of Python and R
  • A basic understanding of Docker (useful but not necessary)

What you'll learn

  • Learn recommended ways to train and deploy 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 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.

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.

  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dell Technologies
  • Hewlett Packard Enterprise
  • AXA

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