Question: I'm looking for a way to version and manage my machine learning models, do you know of any solutions?

If you want a more complete tool to track and version your machine learning models, MLflow is a great option. It's an open-source framework that spans the entire ML workflow, including experiment tracking and model deployment, as well as support for generative AI. MLflow works with popular deep learning frameworks like PyTorch, TensorFlow and scikit-learn and runs on Databricks, cloud computing services and your own laptop.

Another option is Humanloop, which is geared specifically for Large Language Model (LLM) applications and for optimizing them. It's designed to be a collaborative environment for developers and product managers, with version control, evaluation and monitoring tools. Humanloop supports LLM providers and comes with SDKs to integrate with your own code, so it's a good option for teams that want to improve collaboration and AI performance.

Freeplay is another end-to-end lifecycle management tool geared for LLM product development. It's got tools for prompt management, automated batch testing, AI auto-evaluations and human labeling, all in one interface. That makes it a good option for teams that want to streamline ML development and get their workflows moving faster.

For teams that want to build and deploy AI models, Openlayer is a more advanced option with tools for testing, evaluation and observability. It's got automated testing, monitoring and alerts, so it's good for ensuring AI models are reliable and running efficiently. Openlayer supports a range of tasks, including LLMs and text classification, and has free and custom pricing tiers depending on your needs.