Many organizations struggle with how to introduce or mature their ML development efforts. Open model hubs like Hugging Face make it easy for teams to find and explore the potential of new models. And new managed services for ML model development are further simplifying the process for businesses to create models suited to their needs.
But how can DevOps and Security practitioners serve the needs of Data Science and Research Engineers while ensuring the same quality and security standards are applied to the ML development lifecycle as their established SDLC?
In this webinar, Melissa McKay, JFrog Developer Advocate, and Sunil Bemarkar, AWS Sr. Partner Solutions Architect, discuss practical ways to mature your MLOps approach, including bringing model use and development into your existing secure software supply chain and development processes.
Learn more about JFrog’s new ML Model Management capabilities, along with a demo of a new integration between JFrog Artifactory and Amazon SageMaker.
Blog Post: Evolving ML Model Versioning