- Montreal 2020
You got an ML model. How do you manage its lifecycle, and make sure both the model and its data source are versioned so that they can be audited? How do you handle the infrastructure needed to serve your model, and wouldn't it also be nice if the model training, evaluation, and deployment all where automated steps as soon as a change was checked in?
Join me in this session where we will take a deep dive in to MLOps, DevOps for Machine Learning.
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Alexander is a Lead Consultant and .NET Xpert at Excella, based out of Washington DC. He is an international speaker, and have spoken at conferences such as NDC London, ProgNET, Big Data Europe, .NET Jetbrains Global Online Day, Beer City Code, Music City Tech, Code PaLOUsa, and Code On The Beach He's the organizer of the .NET DC User Group and a member of the .NET Foundation. He is passionate about .NET and cloud architecture, and constantly attempts to push his knowledge on data science.