Best Machine Learning Operationalization Software

Machine learning operationalization software allows users to deploy, manage, and monitor machine learning models as they are integrated into business applications. With machine learning optimization tools, businesses can take machine learning models and algorithms built by data scientists and machine learning developers and put them into action. The software provides a way to automate deployment; monitor the health, performance, and accuracy of models; and iterate on those models in a collaborative manner. This enables businesses to scale machine learning across the entire company and make a tangible business impact.

Additionally, these products may provide security, provisioning, and governing capabilities to ensure that only those authorized to make version changes or deployment adjustments can do so. Some machine learning operationalization solutions may offer a way to manage all machine learning models across the entire business, in a single location. These tools are usually language agnostic, so that no matter how an algorithm is built, it can be successfully deployed. However, some may focus specifically on languages like R or Python, among others.

To qualify for inclusion in the Machine Learning Operationalization category, a product must:

  • Offer a platform to deploy, monitor, and manage prebuilt machine learning models
  • Allow users to integrate models into business applications across a company
  • Track the health and performance of deployed machine learning models
  • Provide a holistic management tool to better understand all models deployed across a business

Compare Machine Learning Operationalization Software

Results: 12
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    Algorithmia
    (3)4.5 out of 5
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    Algorithmia is DevOps for machine learning. We power the largest algorithm marketplace as well as mission-critical workloads for enterprise customers. Our technology is trusted by close to 100,000 developers as well as many financial institutions, intelligence agencies, and private companies leveraging AI/ML at scale. Productionizing ML requires a different set of IT infrastructure and workflows than traditional programming. Algorithmia solves this challenge with the AI Layer, an abstraction layer that connects your models, hardware, and applications. The AI Layer allows you to deploy models from any framework, language, or platform and connect to most all data sources. We scale model inference on cloud or on-premises infrastructure with high efficiency and enable users to continuously manage the machine learning life cycle with tools to iterate, audit, secure, and govern. Algorithmia was founded in 2014 by Diego Oppenheimer and Kenny Daniel and is headquartered in Seattle, Washington. It completed a Series B round of funding in May 2019, raising $25 million. Algorithmia currently employs 40-45 people and is growing rapidly.

    5Analytics helps enable companies to integrate, deploy and monitor their machine learning in a scalable, repeatable manner.

    Datatron's platform is vendor, language, and framework agnostic. The hard work begins when your models go into production.

    Datmo enables continuous delivery for data science. Experiment, scale, and deploy without leaving your familiar workflows and deliver results in a fraction of the time.

    An open-source tool and a format for reproducibility and experimentation.

    MLflow is an open source framework to manage the complete Machine Learning lifecycle. Now you can use Managed MLflow as an integrated service with the Databricks Unified Analytics Platform.

    ParallelM's MCenter helps Data Scientists deploy, manage and govern ML models in production. Just import your existing model from your favorite notebook and then create data connections or a REST endpoint for model serving with the drag-and-drop pipeline builder. Advanced monitoring automatically creates alerts when models are not operating as expected due to changing data. With built-in model governance, every action is controlled and tracked including model versioning and who can promote models into production to ensure compliance with regulations.

    MLflow (currently in beta) is an open source platform to manage the ML lifecycle, including experimentation, reproducibility and deployment.

    A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.

    Neptune brings organization and collaboration to data science projects. Everything is secured, backed-up in an organized knowledge repository.

    Numericcal provides tools to help you reach your implementation goals quickly and effortlessly.

    Seldon increases engagement and revenue by providing a smarter personalised user experience.

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