Question: How can I quickly deploy custom ML models to a REST API without worrying about infrastructure scaling?

If you need to get custom ML models up to a REST API as quickly as possible without worrying about scaling infrastructure, Modelbit is a good option. It lets you deploy models to autoscaling infrastructure with built-in MLOps tools, automatic synchronization through Git and industry-standard security. Modelbit supports a variety of environments, including Jupyter notebooks and Snowpark ML, with on-demand, enterprise and self-hosted pricing levels.

Another good option is Replicate, which streamlines running and scaling open-source ML models. It has a library of pre-trained models and lets you deploy your own. With one-click deployment, automatic scaling and usage-based pricing, Replicate lets you add AI abilities without worrying about infrastructure.

Predibase is also worth a look, particularly for fine-tuning and serving large language models. It offers a relatively low-cost serving infrastructure and supports a broad range of models. The service's pay-as-you-go pricing and enterprise-grade security make it a good option for getting AI models into production.

Last, Instill lets you deploy AI models with a no-code/low-code approach that focuses on making data, models and pipelines easy to manage. It comes with features like speech responses, webpage summarization and object detection, and dynamically generated inference API endpoints. Instill's flexible and scalable foundation is good for teams that want to speed up AI work without worrying about infrastructure.