# AI accelerators
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html

> Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.

# AI accelerators

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 use 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.

| Section | Description |
| --- | --- |
| AI integrations and platforms | Templates for end-to-end API workflows between DataRobot and its ecosystem partners (Snowflake, GCP, Azure, AWS, etc.). |
| LLM and GenAI applications | Applications and workflows that leverage large language models (LLM) and generative AI. |
| Model building and fine-tuning | Techniques and workflows for building and tuning machine learning models. |
| Data enrichment and preparation | Workflows for enhancing and preparing data for machine learning. |
| Time series and specific use cases | Workflows and techniques for time series analysis and specific machine learning use cases. |
| Model deployment and MLOps | Workflows for deploying models and managing machine learning operations. |
| Experiment tracking and tuning | Tools and techniques for tracking experiments and tuning models. |
| Model evaluation and metrics | Techniques for evaluating models and understanding their performance metrics. |
| Custom model development | Workflows that integrate custom models with DataRobot. |
| Advanced analytics and tools | Advanced usage of the DataRobot API that you can add to your experiment workflow. |

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