Cloud AI Platform Pipelines vs Slai
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 is a development environment and hosting solution for machine learning models. No servers to manage, no configuration, no headaches. It just works. It is the fastest way to add production-ready ML into an app. |
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 | Production-ready code sandboxes for ML;
One environment for data, dependencies, and code;
Provides source control, giving you a single source of truth for your code and data |
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