BigML vs Cloud AI Platform Pipelines
Overview
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Detailed Comparison
BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services. | 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. |
REST API; bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more; several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more; models are fully actionable -- translated into code that can be cut/paste for local utilization; PredictServer (and Amazon AMI) can be used for real-time or large batch predictions; models can be shared privately or publicly (for free or for a fee set by the developer) | 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 |
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