How to build an agile mobile strategy using APIs

How_to_build
As the number of mobile users grows to 5 billion in 2019, companies are under immense pressure to deliver engaging and secure mobile apps, and not just for consumers. Increasingly, mobile apps are an essential tool to unlock employee innovation and productivity. With MuleSoft’s Anypoint Platform™ businesses can send data securely from any system to mobile, leveraging agile and continuous integration and continuous deployment (CI/CD) best practices to optimize consumer and employee experiences while driving revenue and operational efficiency.
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HOW TO BEGIN YOUR AI JOURNEY

cray

As AI gains traction, organizations are realizing that only larger-scale, enterprise-wide deployments are likely to provide full access to the operational and economic benefits of these new technologies.
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AI for Customer Engagement 101: Where to Get Started

Pega

Hype in the market is one thing. Acceptance is another. Artificial Intelligence (AI) has a lot of buzz, but are customers and businesses ready to embrace it? Join Pega’s Don Schuerman, CTO & VP of Product Marketing, and Dr. Rob Walker, VP of Decision Management & Analytics, as they discuss how to identify AI opportunities with your organization, practical ways in which AI can help win, retain, and grow your customer base, and how to help your customers understand and trust AI technology.
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How Secure Are Your Web Applications?

Tenable

Learn how to secure your essential web apps using automated, accurate and effective web application scanning from Tenable. There are a number of factors increasing cyber risk at the web application layer. DevOps and cloud are in the mix. A bigger issue: web apps are the primary way customers interact with your organization. They’re essential to your business, highly visible and unfortunately, a primary source of data breaches.
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How to Operationalize Machine Learning Projects

Machine Learning (ML) can empower a business and optimize its performance. However, according to Gartner, 85% of data science and machine learning projects fail. These projects aren’t delivering value because companies lack technical skills and have recurring issues with data infrastructure, deployment, and operationalization.
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