Download the free e-book
on Data Science & GenAI on Google Cloud


From tedious coding and data silos to automated, intelligent workflows within a single ecosystem.

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    The Challenges of Modern Data Science,
    , which we solve by combining ML and GenAI

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      Mental friction and context switching

      The traditional workflow requires constantly switching between SQL for data transformation and Python in a local notebook. This is eliminated by a polyglot environment, where the results of SQL queries immediately become a ready-to-use DataFrame in Python.

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      Time-consuming and repetitive data cleaning

      Data scientists waste a lot of time manually preparing and standardizing data. With autonomous AI agents (Data Engineering Agents), you can create complete pipelines, remove duplicates, and build tests using natural language instructions.

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      Insights Hidden in Unstructured Data

      A huge portion of a company’s data (PDFs, images, audio files) is overlooked in traditional analysis. Modern databases allow you to treat files in the cloud as regular SQL tables and directly extract features from them using models such as Gemini.

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      Costly and complex model deployment (MLOps)

      Transitioning a model from the experimental phase to stable production is often the most challenging stage of a project. A centralized model registry and the Feature Store eliminate duplication of engineering work and prevent discrepancies between training and production.