Cloud AI Platform Pipelines vs Neuro
Overview
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Detailed Comparison
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. | It accelerates ML development. It provides an instant infrastructure for your ML projects. You can think of it as the Heroku of MLOps or the AWS lambda functions for ML, all powered by GPUs. |
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 | Wide ML library support;Immediate access to GPUs; Scale as you go; Only pay for compute time |
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