JFrog ML brings devops best practices to building, deploying, managing, and monitoring AI/ML workflows, JFrog said.
JFrog has released JFrog ML, an MLOps solution designed to bring devops best practices to building, deploying, managing, and monitoring AI/ML workflows.
The company said that by pairing practices for developing machine learning models with traditional devsecops processes, organizations can enable development teams, data scientists, and machine learning engineers to build enterprise-ready AI applications, while ensuring that models are seamlessly deployed, secured, and maintained. JFrog ML is the first addition to the JFrog platform resulting from the company’s QWAK.ai acquisition, announced in June 2024.
Announced March 4, JFrog ML helps overcome challenges to the complexity of developing machine learning models by presenting a structured framework designed to support an entire organization and ensuring that models are promoted out of experimental stages, JFrog said. JFrog ML leverages the JFrog Artifactory artifact and model repository and integrates with AI technologies Hugging Face, Amazon SageMaker, Databricks’ MLflow, and Nvidia NIM.
JFrog on March 4 also announced the general availability of JFrog Platform integration with Nvidia NIM, a set of microservices for deploying generative AI models.
