mlops

We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.

Mohan and Alex discuss neurosymbolic AI and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.

We discuss how to monitor models in production, and how it helps you in the long-run.

This week I spoke with Ben Wilson, author of 'Machine Learning Engineering in Action', a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.

This week I spoke with Johnny Greco, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project.

We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.

We put together a list of 48 open-source annotation and labeling tools to support different kinds of machine-learning projects.

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.

Test automation is tedious enough with traditional software engineering, but machine learning complexities can make it even less appealing. Using Deepchecks with ZenML pipelines can get you started as quickly as it takes you to read this article.