Question: Is there a platform that can help me deploy and manage my machine learning models across different environments and libraries?

If you want a foundation for running and managing your machine learning models in different environments and libraries, MLflow is a good option. It's an open-source, end-to-end MLOps platform that helps you build and deploy ML and generative AI applications. MLflow offers experiment tracking, model management, and support for widely used deep learning frameworks like PyTorch and TensorFlow. It's designed to manage the full ML lifecycle, which is why it's a good fit for data scientists and teams trying to improve collaboration and productivity.

Another contender is Anyscale, a platform for building, deploying and scaling AI applications. Based on the open-source Ray framework, Anyscale supports a variety of AI models and can run in the cloud on multiple cloud services and on-premises machines. It offers features like workload scheduling, intelligent instance management and GPU and CPU partitioning for better resource utilization. It also offers native integration with popular integrated development environments and automated workflows for running, debugging and testing code at scale.

Modelbit is another option. It's an ML engineering platform that lets you deploy custom and open-source ML models to autoscaling infrastructure. Modelbit includes built-in MLOps tools for model serving, model registry and industry-standard security. It supports a wide variety of ML models and includes autoscaling compute and tools for alerting, logging and monitoring. The platform is designed to make deployment easier, which is why it's a good fit for teams trying to make ML more efficient.