Keeping up with data — Week 44 reading list

A curious mind wandering through the world of data

CI/CD and automated ML pipeline. Source: MLOps: Continuous delivery and automation pipelines in machine learning

The rise of MLOps continues, so I’m spending a lot of time on MLOps community slack and reading through Awesome MLOps curated list of references trying to keep up. Since this week has been marked by a road rash from an MTB ride, I had more time to read!

Enjoy the weekend with my week’s top 5 reads.

  1. MLOps: Continuous delivery and automation pipelines in machine learning: implementing ML in a production environment doesn’t only mean deploying your model as an API for prediction. Rather, it means deploying an ML pipeline that can automate the retraining and deployment of new models. (Goolge Cloud Articles)

Development and deployment of ML models is arguably getting easier. The question is: are ML engineers and data scientists ready for the associated responsibility? Opportunity is huge, mistakes will be expensive.

Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | CEO & co-founder at DataDiligence.com

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