Question: I need a solution that integrates MLOps tools with model serving, can you suggest something?

If you want a more complete package that combines MLOps tools with model serving, Modelbit is a good option. The service lets you deploy custom and open-source machine learning models to autoscaling infrastructure with MLOps tools built in. It includes model registry, industry-standard security and integration with Git, so you can deploy models from Jupyter notebooks and Snowpark ML. Pricing starts with free on-demand instances and goes up to custom enterprise deals.

Another strong contender is MLflow, an open-source tool that helps you manage the entire ML project lifecycle. It lets you track experiments, log results and manage models, and it supports widely used deep learning and traditional machine learning libraries. MLflow runs on a variety of environments, including Databricks and your own laptop, so it's good for teams that want to improve collaboration and transparency in their work.

If you need a service that handles data curation, model management and pipeline orchestration, Dataloop is worth a look. It includes data management for unstructured data, automated preprocessing and model deployment. Dataloop also has human feedback integration, a marketplace for pre-trained models and strong security controls, designed to help teams collaborate and speed up AI model development.

Last, Replicate is an API-based service that lets you run and scale open-source ML models. It's got a library of production-ready models, automatic scaling, fine-tuning and one-click deployment. That's good for developers who want to add AI abilities without having to worry about infrastructure, with pricing based on hardware usage.