These are also closely related to: "Accelerate K-Means Clustering"

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  Using Machine Learning to predict vulnerabilities in software testing

  By: Qualitest Group

  Type: White Paper

  This white paper examines how machine learning (ML) can improve testing methods. It also discusses the differences in the modern approach to testing which involves both automation and manual testing, and how testing can be improved using machine learning algorithms.

  Highlights from the paper include:

  • ML's role in automating test case creation, script upkeep, and defect forecasting to boost efficiency
  • Using Bayes' Theorem for pinpointing high-risk areas in testing
  • ML in testing shortening regression periods from weeks to days

  Discover more in the full report.

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  5 pillars of an effective AI/ML platform

  By: Red Hat

  Type: White Paper

  AI and 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 AI- and ML-driven apps.

  To effectively use AI, ML, and data science in deployable applications, companies must bring together developers, IT ops, data engineers, data scientists, and ML engineers to operationalize machine learning operations (MLOps).

  In this white paper, you’ll learn about the top 5 considerations you should make for your AI/ML platform. Read on to learn how you can enable your organization to create data-driven apps in a security-focused and collaborative way.

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