ClearML is an open source MLOps platform, and we love the community that’s been growing around us over the last few years. In this post, we’ll give you an overview of the structure of the ClearML codebase so you know what to do when you want to contribute to our community. Prefer to watch the video? Click below: First things first. Let’s take a look at our GitHub page and corresponding repositories. Later on, we’ll cover the most important ones in detail.

Hugging Face is a popular model repository that provides simplified tools for building, training and deploying ML models. The growing adoption of Hugging Face usage among data professionals, alongside the increasing global need to become more efficient and sustainable when developing and deploying ML models, make Hugging Face an important technology and platform to learn and master.

We recently had a chance to catch up with Heather Grebe, Senior Data Scientist at Daupler, which offers Daupler RMS, a 311 response management system, used by more than 200 cities and service organizations across North America and internationally. This platform helps utilities, public works, and other service organizations coordinate and document response efforts while reducing workload and collecting insights into response operations.