Reference architectures for AI and machine learning
Who is this presentation for?
- Data engineers and data scientists
Join Danielle Dean and Wee Hyong Tok to learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Wee Hyong and Danielle detail lessons distilled from working with large global customers on AI/ML projects and the challenges that they overcame.
You’ll learn how to perform batch and real-time scoring of machine learning and deep learning models, how to train a real-time recommendation system on Spark, and the reference architecture for running distributed training across large clusters of GPU machines for distributed training of deep learning models. Code samples will be made available on GitHub.
- A basic understanding of machine learning
What you'll learn
- Learn recommended practices and considerations for scalability, availability, manageability, and security
- See reference architectures for performing batch and real-time scoring of machine learning and deep learning models
Danielle Dean is a principal data scientist lead in AzureCAT within the Cloud AI Platform Division at Microsoft, where she leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.
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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