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README.md
mlops.pdf

README.md

Let's talk about MLOps

Abstract

The quantity of hype around machine learning and AI is probably second only to bitcoins and blockchains.

But until a machine learning model is deployed to production the value delivered to companies is approximately equal to zero.

Together with the common mantra that data science can’t use agile/lean frameworks or that the best software engineering practises don’t apply explains a lot about why often companies got burnt with their data science projects and why generally they under delivered.

MLOps is here to help, the machine-learning equivalent of DevOps: it solves the problems of implementing machine-learning in production.

During this talk I will introduce the data science lifecycle, the concept of machine learning Ops, its characteristics, why is extremely required, how it compares to DevOps, how it will become a required capabilities for any DS/ML/AI Team, tools available and how you can start with it.

Conferences

  • PyCon SK 2019
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