Cloud AI Platform PipelinesCloud AI Platform Pipelines
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 Text & Language Models category of a tech stack.
Key Features
Push-button installation via the Google Cloud ConsoleEnterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and moreSeamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many othersMany prebuilt pipeline components (pipeline steps) for ML workflows, with easy construction of your own custom components
Cloud AI Platform Pipelines Pros & Cons
Pros of Cloud AI Platform Pipelines
No pros listed yet.
Cons of Cloud AI Platform Pipelines
No cons listed yet.
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.
