Speaker "Ed Shee" Details Back



From Model to MIcroservice - Machine Learning at Scale


Until recently, the data science / machine learning field has been pretty immature in it's adoption of DevOps tools and processes. That's now changing rapidly as engineering teams realise that, in order to gain any value from their ML models, they need to get them into production.
In this talk, Ed will introduce the open source Seldon Core library, build a model using popular machine learning tools and deploy it to Kubernetes to handle production traffic.
You will learn how to turn an ML model into a production microservice that handles REST/gRPC traffic, how to use complex model deployment techniques and how to monitor both the infrastructure and the models themselves, spotting drift and outliers as they take place.


Having previously led a tech team at IBM and now Head of Developer Relations at Seldon, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.