AI Accelerators are designed to help speed up model experimentation, development, and production using the DataRobot API. They codify and package data science expertise in building and delivering successful machine learning projects into repeatable, code-first workflows and modular building blocks. AI Accelerators are ready right out-of-the-box, work with the notebook of your choice, and can be combined to suit your needs.
AI accelerators cover a variety of topics, but primarily aim to assist you by:
Providing curated templates for workflows that utilize best-in-class data science techniques to help frame your business problem (e.g., customize a data visualization to your liking or rank models by ROI).
Getting you started quickly on a new AI or ML project by providing necessary insights, problem-framing, and use cases in notebooks.
Fine-tuning your projects and getting the most value from your existing data and infrastructure investments, including third-party integrations.
|Advanced ML and API approaches||Advanced usage of the DataRobot API that you can add to your experiment workflow.|
|Ecosystem integration templates||Templates for end-to-end API workflows between DataRobot and its ecosystem partners (Snowflake, GCP, Azure, AWS, etc.).|
|Generative AI accelerators||Workflows that integrate generative and predictive AI.|
|Use cases and horizontal approaches||Applied approaches to specific business challenges and general frameworks for broad classes of machine learning problems.|
|User AI accelerators||Review user-submitted workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.|