Question: Can you recommend a tool that allows me to create and train AI models securely with dedicated ML services and free compute resources?

If you need a service to build and train AI models with protected ML services and free compute resources, Predibase is a great option. It's a low-cost foundation for fine-tuning and serving large language models, including Llama-2, Mistral and Zephyr. Free serverless inference up to 1 million tokens per day means you can cut computing costs. Predibase also has enterprise-grade security, including SOC-2 compliance, so it's a good option for building and running AI models securely.

Another option is Anyscale, which lets you build, deploy and scale AI applications. It's based on the open-source Ray framework, but Anyscale adds features like workload scheduling, cloud flexibility and heterogeneous node control. It supports a variety of AI models and offers cost savings up to 50% on spot instances. Anyscale also has native integration with popular IDEs and a free tier with flexible pricing, so it should work for many enterprise needs.

For an open-source option, MLflow is a full-featured MLOps tool that spans the life cycle of ML projects. It tracks experiments, manages models and deploys them to different environments. MLflow supports deep learning libraries like PyTorch and TensorFlow, and it's free to use, so it's a good option to improve collaboration and productivity in ML workflows.

Last, Lamini is a powerful service to build, manage and run LLMs. It can be installed and run on premises or in cloud environments, with high-throughput inference and memory tuning for high accuracy. Lamini offers a free tier with limited inference requests and a custom enterprise tier with unlimited tuning and inference, so it should work for teams of different sizes.