NoOps Machine Learning

NoOps Machine Learning

NoOps Machine Learning - A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker. The rapid adoption of Machine Learning, from Big Tech to literally everyone else, resulted in a blooming season for ML tooling and what cool kids now call “MLOps”

A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.

Luigi in a Machine-Learning-first world

“The princess you are looking for is in another castle.”

The rapid adoption of Machine Learning, from Big Tech to literally everyone else, resulted in a blooming season for ML tooling and what cool kids now call “MLOps” (see the thoughtful overviewby Chip Huyen, if you need to catch up). While my LinkedIn feed obsessively repeats that “80% of machine learning projects don’t make it to production”, _I _feel that there actually dozens of strategies to deploy your models, but you’re kind of in the dark _after _you solved that: I am not talking about monitoring, but the general experience of how you develop, experiment, train, iterate in an effective way (i.e. if you still need to solve the production stuff, _this _post won’t really help).

Let’s face it, most ML scripts (well, certainly mine) are pretty messy, especially in the prototype-and-test-live-at-small-scale phase, as they involve stitching together several small tasks: retrieving data, training neural networks, running functional and  behavioral tests etc. In other words, ML projects are  DAG-like, and they need to be replayable, versioned, scheduled and so on: while we have successfully used  Luigi before to orchestrate tasks, we hit some limitations with a growing team and big(ish) neural models.

There is an important phase in ML-driven product development where we should be able to trust our code, but retaining the possibility of making corrections as we go, without worrying about the overall production environment:

before full-scale engineering, ML needs a phase of good-enough engineering, where developers are somehow empowered end-to-end to ship their work to customers, internal users, etc.

serverless machine-learning mlops metaflow aws

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