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JFrog has launched JFrog ML, an MLOps resolution designed to convey devops greatest practices to constructing, deploying, managing, and monitoring AI/ML workflows.
The corporate mentioned that by pairing practices for creating machine learning fashions with conventional devsecops processes, organizations can allow growth 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 creating machine studying fashions by presenting a structured framework designed to assist a whole group and guaranteeing that fashions are promoted out of experimental phases, JFrog mentioned. 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.
