Data science on Google Cloud

A complete suite of data management, analytics, and machine learning tools to generate insights and unlock value from data.

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Why data scientists choose Google Cloud

Google Cloud offers all of the tools data scientists need to unlock value from data. From data engineering to ML engineering, TensorFlow to PyTorch, GPUs to TPUs, data science on Google Cloud helps your business run faster, smarter, and at planet scale.

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The six steps of data science on Google Cloud

Explore training for data scientists

Explore Google Cloud courses on data science from machine learning on analyzing big data, Spark, TensorFlow, and ML pipelines.

Discover reference patterns for industry use cases

Discover code and best practices on using data science to solve your business problems.

Start with Codelabs

Start trying Google Cloud through step-by-step walkthroughs, tutorials, and hands-on coding.

A comprehensive data science toolkit

WORKLOAD	Data science solutions	Key Products
Data discovery and ingestion	Discover and ingest valuable data sources Ingest, process, and analyze real-time or batch data from a variety of sources to make data more useful and accessible from the instant it’s generated.	Data Fusion Data Catalog Pub/Sub BigQuery Data Transfer Service
Data lake and data warehouse	Speed, capacity, and governance at scale Empower your teams to securely and cost-effectively ingest, store, and analyze large volumes of diverse, full-fidelity data.	BigQuery Cloud Storage Dataproc Dataplex
Data preprocessing	Preprocess your data with speed, scale, and ease Prepare your data with serverless and fully managed services. Manage and share your engineered features through a centralized repository.	Dataflow Dataproc
Data analysis and business intelligence	Drive business decisions through data Explore, analyze, visualize, and create dashboards with fully managed tools or customize your analytics environments to suit your needs.	Looker BigQuery Vertex AI Workbench Looker Studio
Machine learning training and serving	Accelerate ML deployment for all levels of expertise Build with the groundbreaking ML tools developed by Google Research. Choose from no-code environments like AutoML, low-code with BigQuery ML, or custom training with Vertex AI and Apache Spark. Bring more models into production to facilitate data-driven decision-making.	Vertex AI AutoML Spark on Google Cloud BigQuery ML
Responsible AI	Build AI that works for everyone Leverage responsible AI practices to inspect and understand AI models, and explainability to help you understand and interpret predictions made by your machine learning models. With these tools and frameworks, you can debug and improve model performance and help others understand your models' behavior.	Explainable AI Model cards TensorFlow open-source toolkit
Orchestration	AI governance through workflows Orchestrate analytic and ML workloads using managed Airflow or Kubeflow Pipelines. Automate, monitor, and govern your ML systems in a serverless manner, and store your workflow's artifacts using Vertex ML Metadata.	Vertex AI Pipelines Cloud Composer Vertex ML Metadata

Discover and ingest valuable data sources

Ingest, process, and analyze real-time or batch data from a variety of sources to make data more useful and accessible from the instant it’s generated.

Speed, capacity, and governance at scale

Empower your teams to securely and cost-effectively ingest, store, and analyze large volumes of diverse, full-fidelity data.

Preprocess your data with speed, scale, and ease

Prepare your data with serverless and fully managed services. Manage and share your engineered features through a centralized repository.

Drive business decisions through data

Explore, analyze, visualize, and create dashboards with fully managed tools or customize your analytics environments to suit your needs.

Accelerate ML deployment for all levels of expertise

Build with the groundbreaking ML tools developed by Google Research. Choose from no-code environments like AutoML, low-code with BigQuery ML, or custom training with Vertex AI and Apache Spark. Bring more models into production to facilitate data-driven decision-making.

Build AI that works for everyone

Leverage responsible AI practices to inspect and understand AI models, and explainability to help you understand and interpret predictions made by your machine learning models. With these tools and frameworks, you can debug and improve model performance and help others understand your models' behavior.

AI governance through workflows

Orchestrate analytic and ML workloads using managed Airflow or Kubeflow Pipelines. Automate, monitor, and govern your ML systems in a serverless manner, and store your workflow's artifacts using Vertex ML Metadata.