Question: I need a tool that supports multiple levels of abstraction for machine learning model development.

For a tool that spans multiple levels of abstraction for machine learning model development, MLflow is a great option. This open-source, end-to-end MLOps platform offers a unified environment for managing the entire ML project lifecycle. It offers tools for experiment tracking, model management and deployment, which makes it a good fit for machine learning practitioners and data scientists who want to collaborate better and work more efficiently.

Another contender is Humanloop, which is designed to manage and optimize the development of Large Language Models (LLMs). It's a collaborative environment for developers, product managers and domain experts to build and iterate on AI features with tools for prompt management, evaluation and model tuning. It's geared for teams that want to streamline their workflow and make AI more reliable.

For teams developing next-gen AI apps, GradientJ is a full suite of tools for ideating, building and managing LLM-native apps. It's got an app-building canvas for quick creation and a team collaboration mechanism for managing and configuring apps after deployment, so you can build and maintain complex AI apps more easily.

Last, Dataloop combines data curation, model management and pipeline orchestration to speed up AI app development. It's got tools for data exploration, automated preprocessing and model deployment, so it's a good option for teams that want to improve collaboration and development speed.