Question: I need a solution that lets me experiment with fine-tuned or open-source AI models and deploy them quickly, while also providing a secure and scalable infrastructure.

If you're looking for a service to run fine-tuned or open-source AI models, and need to get them up and running fast with a secure, scalable foundation, Predibase could be the ticket. It lets developers fine-tune large language models (LLMs) for specific tasks using techniques like quantization and low-rank adaptation that cut costs. The service supports many models, has enterprise-level security with SOC-2 compliance, offers free serverless inference for up to 1 million tokens per day, and charges only for what you use.

Another good option is Anyscale, which offers a full-on platform for developing, deploying and scaling AI applications. Based on the open-source Ray framework, it supports a variety of AI models and comes with features like workload scheduling, cloud flexibility, smart instance management and GPU and CPU fractioning for efficient use of computing resources. Anyscale also comes with powerful security and governance features, so it's a good option for enterprise customers.

Tromero is another good option if you want an AI model training and deployment service that can help you cut costs and keep your data in your control. It makes model fine-tuning and deployment easier with tools like Tailor for quick training and a Playground for fiddling with models. Tromero also comes with scalable and secure GPU Clusters, so it's accessible even to those who don't have AI engineering expertise.

If you want to quickly deploy your custom and open-source machine learning models to autoscaling infrastructure, check out Modelbit. It comes with MLOps tools, Git integration, model registry and industry-standard security. With support for a wide range of ML models and autoscaling compute, Modelbit makes it easy to deploy models via REST API with automatic syncing of model code through Git, which means you can save time and money.