Leverage powerful analytical engines like BigQuery SQL and Apache Spark, then build models using BigQuery ML or Gemini Enterprise Agent Platform. Streamline development with AI-first Colab Enterprise notebook along with robust MLOps, powered by industry-leading AI.

Colab Enterprise offers a secure, managed environment integrated with Gemini Enterprise Agent Platform and BigQuery. Workbenches provide customizable JupyterLab instances, while Cloud Workstations support full IDEs. Extensions also connect self-hosted tools directly to Google Cloud services.

Start with a high-level goal in plain English, and the data science agent generates a detailed plan covering all aspects of data science modeling from data loading, exploration, cleaning, visualization, feature engineering, data splitting, model training/optimization, and evaluation.

AI-assisted data preparation provides suggestions for data cleaning and transformations. The Data Engineering Agent automates data engineering tasks, including ingestion and pipeline creation, through natural language instructions.

Choose any processing engine—whether it's BigQuery's SQL engine or an open-source framework like Apache Spark—to work directly on a single, unified copy of data. This avoids the need to maintain separate data copies for different systems.

BigQuery DataFrames provide a pandas-like API that translates Python code into optimized SQL for execution on the BigQuery engine. This gives the flexibility to use the right tool for the job, whether it's SQL, PySpark, or a pandas-style DataFrame, all while working on the same underlying data

Leverage built-in, pre-trained models, or SQL functions calling Gemini for data analysis/enrichment. For custom models, Agent Platform supports PyTorch, TensorFlow, and other ML libraries. Seamless integration allows feature engineering in BigQuery, custom model training in Agent Platform, and inference back in BigQuery through SQL.

Generate and use multimodal embeddings to perform vector search, enabling semantic understanding and similarity-based retrieval of multimodal data. This allows you to build sophisticated semantic search, recommendation, or segmentation systems without needing to manage a separate, specialized vector database.

Centralize features in the Gemini Enterprise Agent Platform Feature Store to prevent training-serving skew and redundant work. Use AutoML to automate model building for tabular data. All models, whether from BigQuery ML or Gemini Enterprise Agent Platform, are automatically registered in the platform Model Registry. From there, you can easily version, evaluate, and deploy them, creating a seamless end-to-end life cycle on a single platform.