Cracking the machine learning system design code - Part 2

Introductory Series on Engineering Machine Learning Systems.

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• ML Ops originated as a derivative of applying DevOps practices to data science and machine learning workflows or pipelines.

• The term combines ML (machine learning) and operations (Ops).

• At its core, it represents a repeatable process to deploy, monitor, and maintain these pipelines in operational or production systems.

Companies that incorporate this type of rigorous thinking into their data science have a significant competitive edge over those that continually fail to operationalize models and emphasize action above mere insight.

In this video, we’ll get to know how many mechanisms we need to deploy our code into production.