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

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.

It has been months ago when Toloka and ClearML met together to create this joint project. Our goal was to showcase to other ML practitioners how to first gather data and then version and manage data before it is fed to an ML model. We believe that following those best practices will help others build better and more robust AI solutions. If you are curious, have a look at the project we have created together.

What if I want to serve a Huggingface model on ClearML? Where do I start? In general, machine learning engineers know by now that a good model serving engine is invaluable when serving models in production. These days, NVIDIA’s Triton inference engine is a popular option to do so, but it is lacking in some respects.

The popular object detection model and framework made by ultralytics now has ClearML built-in. It’s now easier than ever to train a YOLOv5 model and have the ClearML experiment manager track it automatically. But that’s not all, you can easily specifiy a ClearML dataset version ID as the data input and it will automatically be used to train your model on. Follow us along in this blogpost, where we talk about the possibilities and guide you through the process of implementing them.

Fully automatic retraining loop using ClearML Data Right, so you want to create a fully automatic retraining loop, that you can set up once and then pretty much forget about. Where do we even start?!