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.
Components of AI Platform
This section describes the pieces that make up AI Platform and the primary purpose of each piece.
The AI Platform training service allows you to train models using a wide range of different customization options.
You can select many different machine types to power your training jobs, enable distributed training, use hyperparameter tuning, and accelerate with GPUs and TPUs.
You can also select different ways to customize your training application. You can submit your input data for AI Platform to train using a built-in algorithm (beta). If the built-in algorithms do not fit your use case, you can submit your own training application to run on AI Platform, or build a custom container with your training application and its dependencies to run on AI Platform.
The AI Platform prediction service allows you to serve predictions based on a trained model, 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
This section describes the tools that you use to interact with AI Platform.
Google Cloud Console
You can deploy models to the cloud and manage your models, versions, 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.
gcloud command-line tool
You can manage your models and versions, submit jobs, and accomplish other
AI Platform tasks at the command line with the
gcloud ai-platform command-line tool.
gcloud commands for most AI Platform tasks, and the
REST API (see below) for online predictions.
The AI Platform REST API provides RESTful services for managing jobs, models, and versions, and for making predictions with hosted models on Google Cloud.
You can use the Google API Client Library for Python to access the APIs. When using the client library, you use Python representations of the resources and objects used by the API. This is easier and requires less code than working directly with HTTP requests.
We recommend the REST API for serving online predictions in particular.
User-managed notebooks let you create and manage virtual machine (VM) instances that are pre-packaged with JupyterLab.
User-managed 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 user-managed notebooks instances are protected by Google Cloud authentication and authorization, and are available using a user-managed notebooks instance URL. User-managed notebooks instances also integrate with GitHub so that you can easily sync your notebook with a GitHub repository.
To learn more, see the Notebooks documentation.
Deep Learning VM
Deep Learning VM Images is a set of virtual machine images optimized for data science and machine learning tasks. All images come with key ML frameworks and tools pre-installed. You can use them out of the box on instances with GPUs to accelerate your data processing tasks.
Deep Learning VM images are available to support many combinations of framework and processor. There are currently images supporting TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows.
To see a list of frameworks available, see Choosing an image.
To learn more, see the Deep Learning VM documentation.
- Get started with AI Platform Training and AI Platform Prediction using Keras.
- Learn how to train with custom containers.
- Learn how to train TensorFlow and XGBoost models without writing code by using AI Platform built-in algorithms.
- Learn how to use custom prediction routines to add preprocessing and postprocessing for your online prediction requests.
- Add custom code and custom scikit-learn transformations to your online prediction pipeline.
- Learn more about AI Platform Training and AI Platform Prediction.