Question: Is there a platform that offers a model registry to manage multiple model deployments and training jobs, with built-in security and compliance features?

If you're looking for a platform that has a model registry to manage multiple model deployments and training jobs and has built-in security and compliance, Modelbit is a good option. It's an ML engineering platform with built-in MLOps tools, a model registry and industry-standard security controls like SOC2 compliance and bug bounties. The platform supports a variety of ML models and can deploy them from Jupyter notebooks and Snowpark ML.

Another good option is Dataloop, an AI development platform that includes data curation, model management, pipeline orchestration and human feedback to accelerate AI application development. It has good security controls, including GDPR, ISO 27001 & 27701 and SOC 2 Type II compliance, so it's a good option for deploying and managing models securely. Dataloop also supports a variety of unstructured data types and integrates with popular cloud platforms.

If you prefer an open-source option, MLflow is an end-to-end MLOps platform for streamlining the development and deployment of machine learning and generative AI applications. It includes experiment tracking, model management and logging and supports libraries like PyTorch and TensorFlow. MLflow is free to use and has extensive learning resources, so it's a good option for improving collaboration and efficiency in ML workflows.

Last, Openlayer is a platform for developing, deploying and managing high-quality AI models, in particular large language models. It includes automated testing, monitoring and alerts, as well as SOC 2 Type 2 compliance and on-premise hosting options. It's designed to accelerate model development, collaboration and reliable deployment, so it's a good option for those who need more advanced model management and security controls.