AI Platform (Unified) brings AutoML and AI Platform (Classic) together into a unified API, client library, and user interface. AutoML allows you to train models on image, video, and tabular datasets without writing code, while training in AI Platform (Classic) lets you run custom training code. With AI Platform (Unified), both AutoML training and custom training are available options. Whichever option you choose for training, you can save models, deploy models and request predictions with AI Platform (Unified).
Where AI Platform (Unified) fits in the ML workflow
You can use AI Platform to manage the following stages in the ML workflow:
Define and upload a dataset.
Train an ML model on your data:
- Train model
- Evaluate model accuracy
- Tune hyperparameters (custom training only)
Upload and store your model in AI Platform.
Deploy your trained model and get an endpoint for serving predictions.
Send prediction requests to your endpoint.
Specify a prediction traffic split in your endpoint.
Manage your models and endpoints.
Components of AI Platform (Unified)
This section describes the pieces that make up AI Platform and the primary purpose of each piece.
You can train models on AI Platform (Unified) using AutoML, or use custom training if you need the wider range of customization options available in AI Platform Training.
In custom training, you can select many different machine types to power your training jobs, enable distributed training, use hyperparameter tuning, and accelerate with GPUs and TPUs.
Deploying models for prediction
You can deploy models on AI Platform and get an endpoint to serve predictions on AI Platform.
You can deploy models on AI Platform whether or not the model was trained on AI Platform.
Data labeling service
AI Platform Data Labeling Service (beta) lets you request human labeling for a dataset that you plan to use to train a custom machine learning model. You can submit a request to label your video, image, or text data.
To submit a labeling request, you provide a representative sample of labeled data, specify all the possible labels for your dataset, and provide some instructions for how to apply those labels. The human labelers follow your instructions, and when the labeling request is complete, you get your annotated dataset that you can use to train a machine learning model.
Tools to interact with AI Platform (Unified)
This section describes the tools that you use to interact with AI Platform.
AI Platform Notebooks enables you to create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.
AI Platform Notebooks instances have a pre-installed suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can configure either CPU-only or GPU-enabled instances, to best suit your needs.
Your notebook instances are protected by Google Cloud authentication and authorization, and are available using a notebook instance URL. Notebook instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.
Google Cloud Console
You can deploy models to the cloud and manage your datasets, models, endpoints, and jobs on the Cloud Console. This option gives you a user interface for working with your machine learning resources. As part of Google Cloud, your AI Platform resources are connected to useful tools like Cloud Logging and Cloud Monitoring. The best place to start using the Cloud Console is the Dashboard page of the AI Platform section:
Cloud client libraries
Currently, the AI Platform (Unified) Client Library is available to use with Python. Learn how to install the AI Platform (Unified) Client Library for Python.
Alternatively, you can use the Google API Client Libraries to access the AI Platform API using a variety of different languages, such as Java and Node JS. When using the Google API Client Libraries, you build representations of the resources and objects used by the API. This is easier and requires less code than working directly with HTTP requests.
The AI Platform REST API provides RESTful services for managing jobs, models, and endpoints, and for making predictions with hosted models on Google Cloud.
- Get started by building an image recognition model with AutoML.
- Get started by building an image recognition model with custom training.