...ically follows the completion of the [[training and evaluation]] stages of a machine learning project.
...grating the trained machine learning model into an application, system, or service to enable it to make predictions or decisions. There are various deployment
3 KB (480 words) - 22:26, 21 March 2023
...execution, delivering fast and scalable AI in production environments. As a component of the NVIDIA AI platform, Triton allows teams to deploy, run, an
Triton supports numerous training and inference frameworks such as TensorFlow, NVIDIA TensorRT, PyTorch, Python, ONNX, XGBoost, scikit-learn R
7 KB (964 words) - 16:16, 29 March 2023
...ing NLP. Towards Data Science. https://towardsdatascience.com/hugging-face-a-step-towards-democratizing-nlp-2c79f258c951</ref>
...ources like [[transformers]], [[datasets]], [[tokenizers]], etc. Releasing a wide variety of tools made them popular among big tech companies. <ref name
10 KB (1,398 words) - 12:47, 21 February 2023
| '''[[A*]]''' || || [[A* Search Algorithm]]
| '''[[A/B Testing]]''' || || [[A statistical method for comparing two or more treatments or algorithms]]
34 KB (4,201 words) - 04:37, 2 August 2023
