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README_Commercial Platforms.md

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Commercial Platforms

  • Algorithmia - Cloud platform to build, deploy and serve machine learning models (Video)
  • allegro ai Enterprise - Automagical open-source ML & DL experiment manager and ML-Ops solution.
  • Amazon SageMaker - End-to-end machine learning development and deployment interface where you are able to build notebooks that use EC2 instances as backend, and then can host models exposed on an API
  • bigml - E2E machine learning platform.
  • cnvrg.io - An end-to-end platform to manage, build and automate machine learning
  • Comet.ml - Machine learning experiment management. Free for open source and students (Video)
  • D2iQ KUDO for Kubeflow - Enterprise MLOps platform that runs in the cloud, on premises (incl. air-gapped), or on the edge; based on Kubeflow and open-source KUDO operators.
  • Dataiku - Collaborative data science platform powering both self-service analytics and the operationalization of machine learning models in production.
  • DataRobot - Automated machine learning platform which enables users to build and deploy machine learning models.
  • Datatron - Machine Learning Model Governance Platform for all your AI models in production for large Enterprises.
  • Datmo - Workflow tools for monitoring your deployed models to experiment and optimize models in production.
  • deepsense AIOps - Enhances multi-cloud & data center IT Operations via traffic analysis, risk analysis, anomaly detection, predictive maintenance, root cause analysis, service ticket analysis and event consolidation.
  • Deep Cognition Deep Learning Studio - E2E platform for deep learning.
  • deepsense Safety - AI-driven solution to increase worksite safety via safety procedure check, thread detection and hazardous zones monitoring.
  • deepsense Quality - Automating laborious quality control tasks.
  • Google Cloud Machine Learning Engine - Managed service that enables developers and data scientists to build and bring machine learning models to production.
  • IBM Watson Machine Learning - Create, train, and deploy self-learning models using an automated, collaborative workflow.
  • Labelbox - Image labelling service with support for semantic segmentation (brush & superpixels), bounding boxes and nested classifications.
  • Logical Clocks Hopsworks - Enterprise version of Hopsworks with a Feature Store and scale-out ML pipeline design and operation.
  • MCenter - MLOps platform automates the deployment, ongoing optimization, and governance of machine learning applications in production.
  • Microsoft Azure Machine Learning service - Build, train, and deploy models from the cloud to the edge.
  • MLJAR - Platform for rapid prototyping, developing and deploying machine learning models.
  • neptune.ml - community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
  • Prodigy - Active learning-based data annotation. Allows to train a model and pick most 'uncertain' samples for labeling from an unlabeled pool.
  • Skafos - Skafos platform bridges the gap between data science, devops and engineering; continuous deployment, automation and monitoring.
  • SKIL - Software distribution designed to help enterprise IT teams manage, deploy, and retrain machine learning models at scale.
  • Skytree 16.0 - End to end machine learning platform (Video)
  • Spell - Flexible end-to-end MLOps / Machine Learning Platform. (Video)
  • Talend Studio
  • Valohai - Machine orchestration, version control and pipeline management for deep learning.