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100+ Free Resources On Generative AI for Data Scientists
Awesome Generative AI Data Scientist
The Future is using AI and ML Together
🚀🚀 100+ Free Resources On Generative AI for Data Scientists
A curated list of 100+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI Data Science applications with Large Language Models (LLMs) and deploying LLMs and Generative AI/ML with Cloud-based solutions.
Contributions are welcome! Please submit a pull request or open an issue if you have suggestions for new resources or improvements to existing ones. Thanks for your support!
How to build an agent that can orchestrate the end-to-end process of report planning, web research, and writing. Produces reports of varying and easily configurable formats.
Uber's QueryGPT uses large language models (LLM), vector databases, and similarity search to generate complex queries from English (Natural Language) questions, enhancing productivity for engineers, operations managers, and data scientists.
Tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. A comprehensive guide for building intelligent, interactive AI systems.
A framework for developing LLM applications based on the instruction following, tool usage, planning, and memory capabilities of Qwen. It also comes with example applications such as Browser Assistant, Code Interpreter, and Custom Assistant.
AI researcher that continuously searches for information based on a user query until the system is confident that it has gathered all the necessary details.
A platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can quickly and confidently ship.
A low-code interface to rapidly prototype AI agents, enhance them with tools, compose them into teams, and interact with them to accomplish tasks. Built on AutoGen AgentChat.
A platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can quickly and confidently ship.
LangMem provides tooling to extract important information from conversations, optimize agent behavior through prompt refinement, and maintain long-term memory.
PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
LangSmith is a platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your application, so you can quickly and confidently ship.
Unstructured provides a platform and tools to ingest and process unstructured documents for Retrieval Augmented Generation (RAG) and model fine-tuning.
A web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.).
Open-source framework for creating and managing simulations populated with AI-powered agents. It provides an intuitive platform for designing complex, interactive environments where agents can act, learn, and evolve.
Built on Gradio and supports most of browser-use functionalities. This UI is designed to be user-friendly and enables easy interaction with the browser agent.
Makes it easy to use large language models (LLM) from R. It supports a wide variety of LLM providers and implements a rich set of features including streaming outputs, tool/function calling, structured data extraction, and more.
A high-performance and low-friction chat experience for data scientists in RStudio and Positron–sort of like completions with Copilot, but it knows how to talk to the objects in your R environment.
R interface to various Large Language Models (LLMs) such as OpenAI’s GPT models, Azure’s language models, Google’s Gemini models, or custom local servers.
A simple interface to chat with your favorite AI chatbot from R, inspired by tidymodels where you can easily swap out any ML model for another one but keep the other parts of the workflow the same.
Access various large language model APIs, including Anthropic Claude, OpenAI, Google Gemini, Perplexity, Groq, Mistral, and local models via Ollama or OpenAI-compatible APIs.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon.
Azure AI services help developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and prebuilt and customizable APIs and models.
NVIDIA NIM™, part of NVIDIA AI Enterprise, provides containers to self-host GPU-accelerated inferencing microservices for pretrained and customized AI models across clouds, data centers, and workstations.
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop, and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Reference implementations, example documents, and architecture guides that can be used as a starting point to deploy multiple NIMs and other NVIDIA microservices into Kubernetes and other production deployment environments.
8-Week AI Bootcamp To Become A Generative AI-Data Scientist
Focused on helping you become a Generative AI Data Scientist. Learn how to build and deploy AI-powered data science solutions using LangChain, LangGraph, Pandas, Scikit Learn, Streamlit, AWS, Bedrock, and EC2.
A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs
