These are also closely related to: "Operationalize Machine Learning"

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  Top 5 considerations for your AI/ML platform

  By: Red Hat

  Type: White Paper

  Artificial intelligence (AI) and machine learning (ML) are essential for today’s organizations, and data is just as critical to applications as the code they are built on.

  But there is still a lack of collaboration between the different groups involved in the development of these applications. To effectively harness AI, ML, and data science, companies must bring together developers, IT operations, and engineers to operationalize machine learning operations (MLOps).

  Use this checklist, featuring the top 5 considerations for your AI/ML platform, to implement MLOps processes that help your teams start creating data-driven applications in a security-focused and collaborative way.

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  4 steps for operationalizing machine learning (ML) models

  By: Red Hat

  Type: White Paper

  There’s no single strategy that organizations use to build and operationalize machine learning (ML) models. However, regardless of which approach you take, what’s consistent between them all is the need to gather and prepare data, develop models, turn models into intelligent applications, and derive revenue from those applications.

  In this white paper, you’ll discover how adopting machine learning operations (MLOps) practices can save time and money in your efforts to build, deploy, and manage machine learning models and applications. Read on to learn about 4 steps you can take to support reliable MLOps across your business environment.

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