Implementing of Artificial intelligence (AI) has changed from being a new fad to a serious organizational objective by 2022. Businesses of all sizes and stages of development are discovering possibilities to adopt AI. Organizations can modify core functions with this deployment, which shows promising outcomes in terms of effectiveness and efficiency. AI has permeated every sphere of our civilization and way of life over the past ten years.

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.