| 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. |
| 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." |
| Create an LLM multimodal PDF RAG |
Use an LLM as an OCR tool to extract all the text, table, and graph data from a PDF, then build a RAG and playground chat. |