What is the event about?
Until recently, the Data Science and Machine Learning (ML) field has been under utilised in its adoption of DevOps tools and processes However that's now changing, as engineering teams gain any value from their Machine learning models, by 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 (Representational State Transfer Application Programming Interface)/gRPC (Google Remote Procedure Call) 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.

Who is it aimed at:
Data Scientists, DevOps Engineers or ML Engineers

Why should you attend?
Getting ML models into production is the hardest and most in-demand skill in machine learning right now. This talk teaches you how to do that.

Speaker Bio:
Ed Shee, Head of Developer Relations at Seldon
With a background in cloud computing and a passion for machine learning, Ed has combined those skills to become a leader in the field of MLOps (machine learning operations). Organiser of Tech Ethics London and MLOps London, Ed is heavily involved in lots of developer communities and, thankfully, loves both beer and pizza.