& Principles of MLOps

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 Build your own  Machine Learning Serverless Prediction Service

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Intended Audience

This course is intended for new and seasoned practitioners of Machine Learning and Data Science with some level of experience programming in Python and working in notebooks environment.

In this course, you will build a prediction service, not just train a model. This will take you to the next step in Machine Learning (ML).

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& good to have

This course is built with the most easy and least technological friction possible; you will need a working knowledge of Python and a Github account.

Extra tools, frameworks and software will be provided during certain modules and will always be following the principle of being free, and sustainably accessible (not temporary accounts or limited access).

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What you will learn

This course is intended to move beyond the classic MLOps gap where data scientists and ML practitioners create models and have difficulties using those models for real applications and services.

In this course; you will learn how to produce such prediction services, end-to-end, with state of the art technologies... and for free! 

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Operate AI-Enabled Services

Learn to develop and operate AI-enabled (prediction) services on serverless infrastructure

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MLOps Fundamentals

Learn MLOps fundamentals: versioning, testing, data validation, and operations

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Serverless Feature Pipeline

Develop and run serverless feature pipelines 


Meet your teacher!

Jim Dowling
Associate Professor at KTH royal institute of technology & CEO of Hopsworks.