Reference architectures for AI and machine learning
Who is this presentation for?
- Data engineers and data scientists
Join Danielle Dean, Mathew Salvaris, and Angus Taylor to learn best practices and reference architectures (which have been validated in real-world AI and ML projects for customers globally) for implementing AI. They detail lessons distilled from working with large global customers on AI and 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
Mathew Salvaris is a senior 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 and devised models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and he held a postdoctoral position at the University of Essex’s Brain Computer Interface group and was a visiting researcher at Caltech. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.
Angus Taylor is a data scientist at Microsoft, where he builds AI solutions for customers. He holds a MSc in artificial intelligence and has previous experience in the retail, energy, and government sectors.
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