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DataRobot API resources > AI accelerators > Generative AI accelerators

Generative AI accelerators

Topic Description
Smart cluster labeling using generative AI Use cluster insights provided by DataRobot with ChatGPT to provide business- or domain-specific labels to the clusters using OpenAI and DataRobot APIs.
Improve customer communication using generative AI How generative AI models, like GPT-3, can be used to augment predictions and provide customer-friendly subject matter expert responses.
Hyperparameter optimization workflow Build on the native DataRobot hyperparameter tuning by integrating the hyperopt module into DataRobot workflows.
Zero-shot text classification for error analysis Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models.
Optimize customer support workflows with generative AI Use generative AI models to cater to level-one requests, allowing support teams to focus on more pressing and high-visibility requests.
Monitor generative AI with custom metrics Monitor LLMs and generative AI solutions to measure alignment and ROI and to provide guardrails.
Use the JITR Bot to generate context-aware responses Create a deployment to provide context-aware answers 'on the fly' using "Just In Time Retrieval", or JITR for short.
Build a healthcare conversation agent using medical research Use Retrieval Augmented Generation to build a conversational agent for Healthcare professionals.
Enable observability in large language models Enable LLMOps or observability in your existing Generative AI solutions without refactoring code.
Automate product feedback reports using generative AI Use Predictive AI models in tandem with Generative AI models to overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text.
Create article summaries from RSS feeds Learn how to summaries articles in a Streamlit app via an RSS feed.
Fine-tuned Llama 2 on Google GCP and DataRobot Learn how to integrate Llama 2 on Google GCP and DataRobot.
Enterprise chatbots for Teams and Slack Build collaborative app plug-ins, such as bots for Teams and Slack.
Use Google Gemini with DataRobot Leverage LLMs proposed by hyperscalers via the Custom Model Workshop.
Use an LLM custom inference model template The LLM custom inference model template enables you to deploy and accelerate your own LLM, along with "batteries-included" LLMs like Azure OpenAI, Google, and AWS.
Use DataRobot generative AI with Microsoft Teams With DataRobot's Generative AI offerings, organizations can deploy chatbots without the need for an additional front-end or consumption layers.
Mistral 7B on Google GCP and DataRobot Learn how to integrate Mistral 7B on Google GCP and DataRobot.
Create a vectorstore chunk visualization app Implement a Streamlit application to gain insights from a vector database of chunks.
Use the AI data preparation assistant Use the AI data preparation assistant to streamline and automate the data preparation process.
Fine-tune and deploy LLMs in a codespace Review an end-to-end workflow for fine-tuning and deploying an LLM using features of Hugging Face, Weights and Biases (W&B), and DataRobot.
Everything of Thoughts Implementation Implement and evaluate Everything of Thoughts (XoT) in DataRobot, an approach to make generative AI "think like humans."
Updated February 20, 2025