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.
Like humans, Machine Learning (ML) models can recognize intricate patterns and anticipate the outcome of new data. On some natural language problems, ML models have even surpassed human performance. But much like people, ML models are susceptible to error. For every ML application in the real world, estimating how frequently a model will be inaccurate is essential. Intuitively presenting information and emphasizing the best ways to enhance a model are equally important.
Iguazio users can now run their ML workloads on AWS EC2 Spot instances. When running ML functions, you might want to control whether to run on Spot nodes or On-Demand compute instances. When deploying Iguazio MLOps platform on AWS, running a job (e.g. model training) or deploying a serving function users are now able to choose to deploy it on AWS EC2 Spot compute instances.
Machine Learning (ML) and Artificial intelligence (AI) are at the center of the hyper-competitive era in which change occurs with new technologies in the span of a single blink of an eye. Modern innovations like AI, predictive analytics, ML, and other digital disruptors are changing how businesses operate and how customers interact with brands in every sector of the economy. Moments of existential transition are becoming common for organizations.
