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.
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.
