The Framework Way is the Best Way: the pitfalls of MLOps and how to avoid them (24 May 2022)
As our AI/ML projects evolve and mature, our processes and tooling also need to keep up with the growing demand for automation, quality and performance. But how can we possibly reconcile our need for flexibility with the overwhelming complexity of a continuously evolving ecosystem of tools and technologies? MLOps frameworks promise to deliver the ideal balance between flexibility, usability and maintainability, but not all MLOps frameworks are created equal. In this post, I take a critical look at what makes an MLOps framework worth using and what you should expect from one.
All Continuous, All The Time: Pipeline Deployment Patterns with ZenML (11 May 2022)
Connecting model training pipelines to deploying models in production is seen as a difficult milestone on the way to achieving MLOps maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
Everything you ever wanted to know about MLOps maturity models (07 Mar 2022)
An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.
MLOps: Learning from history (09 Nov 2020)
MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.
12 Factors of Reproducible Machine Learning in Production (28 Sep 2020)
A set of guiding principles to help you better productionize your machine learning models.
Why ML in production is (still) broken - [#MLOps2020] (26 Jun 2020)
The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
A case for declarative configurations for ML training (17 May 2020)
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
Why deep learning development in production is (still) broken (01 Mar 2020)
Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.