AI/ML
Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial
intelligence platform
Use AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and
to use your model to make predictions about new data
Where AI Platform fits in the ML workflow
The diagram below gives a high-level overview of the stages in an ML workflow. The blue-filled boxes indicate where AI Platform provides managed services and APIs:
As the diagram indicates, you can use AI Platform to manage the following stages in the ML workflow:
- Train an ML model on your data:
- Train model
- Evaluate model accuracy
- Tune hyperparameters
- Deploy your trained model.
- Send prediction requests to your model:
- Online prediction
- Batch prediction (for TensorFlow only)
- Monitor the predictions on an ongoing basis.
- Manage your models and model versions.
Data science
A complete suite of data management, analytics, and machine learning tools to generate insights and unlock value from data
AI Infrastructure
- Options for every business to train deep learning and machine learning models cost-effectively.
- AI accelerators for every use case, from low-cost inference to high-performance training
- Iterate faster with high-performance Cloud GPUs and Cloud TPUs
- Simple to get started with a range of services for development and deployment
Responsible AI
AI is transforming industries and solving important, real-world challenges at scale. This vast opportunity carries with it a deep responsibility to build AI that works for everyone