End-to-end platform for data science and machine learning
AI Platform makes it easy for developers, data scientists, and data engineers to streamline their ML workflows. Whether it is point-and-click data science using AutoML or advanced model optimization, AI Platform helps all users take their projects from ideation to deployment seamlessly. Take advantage of Google’s expertise in AI by infusing our cutting-edge AI technologies into your applications via tools like TPUs and TensorFlow.
Prepare and store your datasets with BigQuery, then use the built-in Data Labeling Service to label your training data by applying classification, object detection, and entity extraction, etc., for images, videos, audio, and text.
Build best-in-class machine learning models without writing a single line of code with AutoML and its easy-to-use UI, or with your own code with AI Platform Notebooks, a managed Jupyter Notebook service that provides fully configured environments for model development. You can then train your models in the cloud with AI Platform Training.
Validate your model with AI Explanations that helps you understand your model's outputs, verify the model behavior, recognize bias in your models, and get ideas for ways to improve your model and your training data. To take a step further, use AI Platform Vizier, a black-box optimization service, to tune hyperparameters and optimize your model’s output.
Deploy your models at scale and get predictions from them in the cloud with AI Platform Prediction that manages the infrastructure needed to run your model and makes it available 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 your TensorFlow instance.
Manage your models, experiments, and end-to-end workflows with MLOps by deploying robust, repeatable pipelines with AI Platform Pipelines. Continuous evaluation helps you monitor the performance of your models and provides continual feedback on how your models are performing over time.
End-to-end machine learning life cycle
Explainable AI, Notebooks, Vizier, TensorFlow Enterprise, and Pipelines (Marketplace) can be used for no charge, but you do pay for any Google Cloud resources, such as Compute and Storage you use with them. You can learn about the pricing of our managed services like Training, Prediction, Data Labeling Service, AutoML, and BigQuery here. You can also use our pricing calculator to estimate the costs of running your workloads.
The ability to use Google Cloud Platform to perform image analysis on Cloud ML Engine for epidemiologic breast cancer studies represents a huge step forward. Applying analysis to human pathology is a very new field, and we’re excited about what we’ll find. If we hadn’t taken a machine learning approach, it would have taken us three years instead of three months to analyze over 1,700 tissue samples stored in Cloud Storage, even with a team of dedicated pathologists, and because people bring their own bias to any analysis, we’re also achieving better consistency and quality.- Mia M. Gaudet, PhD, Scientific Director of Epidemiology Research, American Cancer Society
In retail, it’s important to provide customers with easy access to alternative products or recommended add-ons. We train our own machine learning models with TensorFlow on Google Cloud ML, and we automate the periodic retraining of these models with Kubeflow Pipelines. Together with AI Hub, useful for sharing models between data scientists, we can now iterate faster on our models, and automatically deploy them to staging and production.- Lucas Ngoo, co-founder, CTO, Carousell
Google Cloud machine learning partners come with deep AI expertise and can help you incorporate ML for a wide range of use cases across every stage of model development and serving.
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