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Cloud AI Platform Pipelines

Deploy robust, repeatable machine learning pipelines
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What is Cloud AI Platform Pipelines?

It provides a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility, and delivers an enterprise-ready, easy to install, secure execution environment for your ML workflows.
Cloud AI Platform Pipelines is a tool in the Machine Learning as a Service category of a tech stack.

Cloud AI Platform Pipelines Integrations

Google BigQuery, Google Kubernetes Engine, Google Cloud Functions, Google Cloud Dataflow, and Google AI Platform are some of the popular tools that integrate with Cloud AI Platform Pipelines. Here's a list of all 5 tools that integrate with Cloud AI Platform Pipelines.

Cloud AI Platform Pipelines's Features

  • Push-button installation via the Google Cloud Console
  • Enterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and more
  • Seamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many others
  • Many prebuilt pipeline components (pipeline steps) for ML workflows, with easy construction of your own custom components

Cloud AI Platform Pipelines Alternatives & Comparisons

What are some alternatives to Cloud AI Platform Pipelines?
Amazon SageMaker
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
See all alternatives

Cloud AI Platform Pipelines's Followers
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