JFrog has launched JFrog ML, an MLOps resolution designed to carry devops finest practices to constructing, deploying, managing, and monitoring AI/ML workflows.
The corporate stated that by pairing practices for growing machine studying fashions with conventional devsecops processes, organizations can allow improvement groups, information scientists, and machine studying engineers to construct enterprise-ready AI functions, whereas guaranteeing that fashions are seamlessly deployed, secured, and maintained. JFrog ML is the primary addition to the JFrog platform ensuing from the corporate’s QWAK.ai acquisition, introduced in June 2024.
Introduced March 4, JFrog ML helps overcome challenges to the complexity of growing machine studying fashions by presenting a structured framework designed to help a complete group and guaranteeing that fashions are promoted out of experimental levels, JFrog stated. JFrog ML leverages the JFrog Artifactory artifact and mannequin repository and integrates with AI applied sciences Hugging Face, Amazon SageMaker, Databricks’ MLflow, and Nvidia NIM.
