mlops

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.

ML practitioners today are embracing data-centric machine learning, because of its substantive effect on MLOps practices. In this article, we take a brief excursion into how data-centric machine learning is fuelling MLOps best practices, and why you should care about this change.

Getting started with distributed ML in the cloud: How to orchestrate ML workflows natively on Amazon Elastic Kubernetes Service (EKS).

Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations 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.

I explain why data labeling and annotation should be seen as a key part of any machine learning workflow, and how you probably don't want to label data only at the beginning of your process.