Managed services for hassle-free development
Faster time to production with code-based and no-code tools
Robust governance with interpretable models
For every skill level
Whether it's point-and-click data science using AutoML or advanced model optimization, AI Platform helps all users take their projects from ideation to deployment, quickly and seamlessly.
Machine learning doesn’t stop at deployment. AI Platform makes it easy for developers, data scientists, and data engineers to streamline and scale their ML workflows.
Best of Google's AI
Take advantage of Google’s expertise in AI by infusing our cutting-edge technologies into your applications via tools on AI Platform like TPUs and TensorFlow.
Prepare and store your datasets with BigQuery and Cloud Storage, then use the built-in Data Labeling Service to label your training data for classification, object detection, entity extraction, and other objectives for image, video, tabular, and text data.
Build best-in-class ML models without writing any code with AutoML's easy-to-use UI, or using your own code written in Notebooks, a managed Jupyter Notebook service. Use the latest open-source deep learning frameworks on Deep Learning VM Image or Deep Learning Containers. Then train your models with our fully managed Training service.
Validate your model with AI Explanations and What-If Tool, which help you understand your model's outputs, verify model behavior, identify bias, and find ways to improve your model and training data. Take model tuning a step further using Vizier, a black-box optimization service, to tune hyperparameters and optimize your model’s performance.
Deploy your models at scale to get predictions in the cloud with Prediction, which hosts your model for online and batch prediction requests. You can also use AutoML Vision Edge to deploy your models at the edge and trigger real-time actions based on local data. TensorFlow Enterprise offers enterprise-grade support for TensorFlow instances.
Manage your models, experiments, and end-to-end workflows with Pipelines by applying MLOps best practices with robust, repeatable pipelines. Continuous evaluation helps you monitor your models' performance, and provides continual feedback over time.
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Docs, quickstarts, and more
Introduction to AI Platform (Classic)
Fundamentals of AI Platform (Classic) and how it fits in your ML workflow.
Training and prediction with TensorFlow Keras
How to train a neural network on AI Platform using the Keras sequential API and how to serve predictions from that model.
Creating a notebook
See how you can create a AI Platform Notebook (JupyterLab), a DLVM instance with the latest machine learning and data science libraries.
Techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for ML systems.
Migrate your custom ML models to Google Cloud in three steps
Overview of ML Pipeline Generator and the expected user journey, and instructions for orchestrating a training job on AI Platform.
Introduction to AI Platform (Unified)
Learn about AI Platform (Unified), which brings AutoML and AI Platform (Classic) together into a unified API, client library, and user interface.
AI Platform (Unified) for AI Platform (Classic) users
Comparison of AI Platform (Unified) and AI Platform (Classic) for users who are familiar with AI Platform (Classic).
All your AI tools in one platform
|AI Explanations||Understand how each feature in your input data contributed to model's outputs.|
|AutoML||Easily develop high-quality custom machine learning models without writing training routines. Powered by Google’s state-of-the-art transfer learning and hyperparameter search technology.|
|Continuous evaluation||Obtain metrics about the performance of your models in production. Compare predictions with ground truth labels to gain continual feedback and optimize model performance over time.|
|Data Labeling Service||Get highly accurate labels from human labelers for better machine learning models.|
|Deep Learning Containers||Quickly build and deploy models in a portable and consistent environment for all your AI applications.|
|Deep Learning VM Image||Instantiate a VM image containing the most popular AI frameworks on a Compute Engine instance without worrying about software compatibility.|
|Notebooks||Create, manage, and connect to VMs with JupyterLab, the standard data scientist workbench. VMs come pre-installed deep learning frameworks and libraries.|
|Pipelines||Implement MLOps by orchestrating the steps in your ML workflow as a pipeline without the difficulty of setting up Kubeflow Pipelines with TensorFlow Extended (TFX).|
|Prediction||Easily deploy your models to managed, scalable endpoints for online or batch predictions.|
|TensorFlow Enterprise||Easily develop and deploy TensorFlow models on Google Cloud with enterprise-grade support and cloud scale performance.|
|Training||Train any models in any framework on any hardware, from single machines to large clusters with multiple accelerators.|
|Vizier||Optimize your model's output by intelligently tuning hyperparameters.|
|What-If Tool||Visualize your datasets and probe your model to better understand its behavior with an interactive visual interface.|