Machine Learning System Design - Part 3
Channel: CafeIO
Date: 2020-07-12

This is the Part 3 of my 3 part Introductory Series on Engineering Machine Learning Systems. 

Do Check out Other Parts in the Series. 

► Part 1 - https://youtu.be/sgZ6S85T3d4
► Part 2 - https://youtu.be/CMPso-eMyaw
► Part 3 - https://youtu.be/NjryhsJkH2A

#mlops #machinelearning #container #docker #kubernetes
#datascience #architecture #cloud

Excerpt from OReilly's Book on MLOps.
 
" 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 build this kind of methodical thinking into their data science have a big competitive advantage over those that consistently fail to operationalize models and that fail to prioritize action over mere insight "

Link to the Book - https://www.oreilly.com/library/view/ml-ops-operationalizing/9781492074663/ 

References

► https://towardsdatascience.com/being-a-data-scientist-does-not-make-you-a-software-engineer-c64081526372
► https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7