Snorkel Flow provides a comprehensive solution for automating the training data pipeline in machine learning projects. By leveraging weak supervision and model-driven annotations, it allows users to generate large volumes of labeled data quickly and efficiently. Users can collaborate on building, testing, and refining machine learning models, ensuring that data quality remains high while minimizing manual labeling efforts. Whether you're working on natural language processing, image classification, or other data-centric tasks, Snorkel Flow streamlines the process.

DataAgent leverages advanced AI agents built on top of LLMs to explore datasets, generate insights, and assemble machine learning pipelines automatically. Users point DataAgent at a CSV, SQL table, or Pandas DataFrame and pose questions in natural language. The agent interprets queries, executes analysis code, visualizes results, and even writes modular Python scripts for ETL and modeling tasks. It streamlines the entire data science workflow by reducing boilerplate coding and accelerating experimentation.