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IBM Watson Studio is built to be an enabler of self-service, helping data scientists bring AI, machine learning, and deep learning to life. Read this report to understand how IBM adds in functionality around governance and data prep as well as increased capabilities around deep-learning-as-a-service, automated model testing, drag-and-drop neural network design using popular open frameworks, and embedded, pretrained, customizable Watson APIs. Learn more.
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This practical eBook walks you through Kubernetes security features—including when to use what—and shows you how to augment those features with container image best practices and secure network communication. Learn more.
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Application Performance Monitoring (APM) is nothing new, but innovations in this space have made APM more insightful, more detailed and more actionable in its ability to improve a team's ability to produce flawless customer experiences. Learn more.
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IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Watson Studio and Watson Knowledge Catalog. The results? A projected ROI of 459%, over three years, achieved in less than six months. Learn more.
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How aware are you of hidden bias in your machine learning models and neural networks? A biased model can harm your customers, your brand and your business if it results in unintended, unexplainable actions against individuals or groups. Learn more.
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Learn about our latest data quality and enrichment APIs, range vs. point data geocoding, matching strategies for global records, data integration platform comparisons, GDPR, and other articles on data challenges, big and small.
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Build, run and manage your AI in your enterprise, with trust and transparency – to drive business value. New trust and transparency capabilities from IBM represent the cornerstone of how we’re helping businesses build, run and manage AI models and applications across their organizations. Learn more.
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In our 24-criteria evaluation of multimodal predictive analytics and machine learning (PAML) providers, we identified the 13 most significant ones — Dataiku, Datawatch, FICO, IBM, KNIME, MathWorks, Microsoft, RapidMiner, Salford Systems (Minitab), SAP, SAS, TIBCO Software, and World Programming — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise application development and delivery (AD&D) leaders make the best choice. Learn more.
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The democratization of machine learning platforms is proliferating analytical assets and models. The challenge now is to deploy and operationalize at scale. Data and analytics leaders must establish operational tactics and strategies to secure and systematically monetize data science efforts. Learn more.
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The democratization of machine learning platforms is proliferating analytical assets and models. The challenge now is to deploy and operationalize at scale. Data and analytics leaders must establish operational tactics and strategies to secure and systematically monetize data science efforts. Learn more.
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We all use APIs every day. The demands of digital transformation, and the related need for platforms and ecosystems, make it essential to manage APIs throughout their life cycle. We identify the pros and cons of a wide range of API management vendors and offerings, to help you make the right choice. Learn more.
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This e-book details an architecture called agile integration, consisting of three technology pillars—distributed integration, containers, and APIs—to deliver flexibility, scalability, and reusability. Learn more.