The promise of https://thenewstack.io/category/machine-learning/ (ML) models are proliferating enterprises, but deploying them to the cloud or edge computing environments is proving to be a significant challenge to scale across a plethora of tools and frameworks. From automating machine learning models to design, management, and optimization, the ability to quickly deliver these models into production while saving costs and sustaining them over time is challenging developer productivity. “Machine learning models are very compute-intensive and once you’re ready to deploy a model, it requires you to understand the hardware and really tune in to optimize it to get it ready for deployments,” said Ceze.
By using machine learning, Apache TVM helps companies streamline the insights needed to deploy machine learning models, Ceze said. “Once you get a machine learning model, there’s billions of ways in which you can actually produce the code to represent your model and run it on the target hardware.”