Integrated solutions that handle the end-to-end process of developing, training, deployment, and monitoring of machine learning models.

Platforms and frameworks that help developers build and train machine learning models.

Direct providers and marketplaces of data that’s required to train machine learning models. This also includes providers of synthetic data generation platforms and data labeling services.

Platforms that deploy and host the trained and tested models in production and integrate it into applications though APIs.

Platforms that monitor deployed machine learning models to track model performance, identify drifts, and other unknown issues.

Makers of hardware such as central processing units (CPU), graphics processing units (GPU), and other application specific hardware required to train and run machine learning workloads.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

Overview

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  <p>
    Machine learning (ML) infrastructure refers to the software and hardware solutions that help enterprises develop ML models and apply AI to their business processes and end products.
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  <p>
    Software-based ML infrastructure includes platforms that 1) help developers build and train AI and ML models, 2) facilitate the data needed to train these models, and 3) develop solutions to deploy, serve, and monitor these models in production. Platforms in this space target each segment individually or cater to the end-to-end ML operations (MLOps) lifecycle.
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  <p>
    Hardware elements that support the ML lifecycle cover application-specific processors, central processing units (CPUs), and graphics processing units (GPUs), which are used to train models and run AI, ML, and deep learning workloads.
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  <p>
    
    Note: Additional sections (market sizing, detailed overview, incumbents, etc.) can be provided on request.
  </p>
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Machine Learning Infrastructure

Machine Learning Infrastructure

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