Systems | Development | Analytics | API | Testing

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.

For the next interview in our series speaking to AI leaders from around the world, we’ve welcomed experienced AI Specialist Speaker, Monish Gandhi of Gradient Ascent.

For the next interview in our series speaking to technical leaders from around the world, we’ve welcomed Hikari Senju, Founder and CEO of Omneky. Hikari Senju is the founder and chief executive officer of Omneky, an AI platform for generating, analyzing, and optimizing personalized ads at scale.

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.

In the latest instalment of our interviews speaking to leaders throughout the world of tech, we’ve welcomed Ricardo Michel Reyes, Chief Science Officer at Erudit AI.

For our latest AI specialist interview on our blog, we’ve welcomed Adam Honig, CEO of Spiro.AI.

In this blog post, we’ll be taking a closer look at Hyper-Datasets, which are essentially a supercharged version of Clear-ML Data.

A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. I recommend you read the entire piece, but to me the key takeaway – AI at scale isn’t magic, it’s data – is reminiscent of the 1992 presidential election, when political consultant James Carville succinctly summarized the key to winning – “it’s the economy”.

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Huggingface model and deploy it on the Nvidia Triton inference engine. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. Our developers know this, of course, so ClearML Serving uses Nvidia Triton on the backend if a model needs GPU acceleration.