We recently rolled out our very own GPU autoscaler in Collaboration with Genesis Cloud and it has been quite a success. Also recently, YOLOv8 by Ultralytics was unveiled, the new king of object detection, segmentation and classification. In this blogpost we’ll see that you can train a computer vision model using the ClearML/Genesis Cloud autoscaler at a fraction of the cost of competing cloud services like AWS or GCP. And it even runs 100% off of green energy! 😎

10 years ago, it would be ridiculous for people to believe that someday they would be able to use their faces to unlock their phones. That’s because it had been extremely difficult to create cartoon characters without profound drawing skills – but now we can easily turn photos into cartoon characters. Struggling with parallel parking? No worries, because self-parking systems are becoming standard equipment in vehicles.

Deep learning has become a buzzword in the field of artificial intelligence (AI) in recent years. It has achieved impressive results in a variety of tasks, including image and speech recognition, natural language processing, and even playing games.

Improving patient care is right up there with the importance of optimizing the allocation and efficiency of precious resources when it comes to today’s healthcare. But that’s been difficult for people alone to accomplish, even with automation. The good news is that machine learning is now addressing these challenges and a company called Iodine Software is leading the innovation.

By Victor Sonck, Developer Advocate at ClearML Reporting your findings to colleagues, managers, or even your future self is a core component of any modern ML workflow. Having one central place where you can easily build a summary of your work makes it that much easier. That’s where ClearML Reports come in.

By Vidushi Meel, Freelance Technical Writer What is MLOps? Learn what Machine Learning Operations are, how it works, why it’s needed, the advantages of using MLOps, and why MLOps matters in this complete guide.

As the utilization of machine learning and MLOps (machine learning operations) continues to gain traction within organizations, it is imperative to stay abreast of the latest advancements and developments in the field.

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Hugging face model and deploy it on the Nvidia Triton inference server. 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. If you haven’t read the blogpost yet, do it now first, I will be referencing it quite a bit in this blogpost.

ClearML is now officially integrated into the NVIDIA TAO Toolkit 🎉. For those of you that don’t know yet, the NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training process by abstracting away the AI/deep learning framework complexity.

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.