Systems | Development | Analytics | API | Testing

Varun Jasti of AWS explains why real-time data—not better models—is the true unlock for enterprise AI. Most enterprises don't need to build AI models from scratch—they need to put AI to work. That requires a data foundation that is real-time, reliable, and ready to serve intelligent systems at scale.

Hey, did you know Cloudera is actively hiring to build the next phase of enterprise AI? While much of the industry is focused on experimentation, Cloudera is investing in execution, scaling real-world AI with innovations like Cloudera Agent Studio and managing data at exabyte scale. As we continue to bring AI to data anywhere, we’re growing our global team to turn AI from pilot to production.

As organizations race to capitalize on AI, the foundation of success lies in trusted data and scalable infrastructure. In this video, we explore how Cloudera AI, powered by NVIDIA, delivers an end-to-end platform that enables organizations to build, test, and deploy high-performance AI solutions. From the Cloudera hybrid data lake to production-ready AI, discover how Cloudera is helping enterprises accelerate their data-driven future.

Testing complex UI elements like CAD software, Google Maps, or Citrix environments often leads to brittle tests and false negatives. Vision AI solves these automated testing challenges by recognizing elements just like a human would, reducing manual testing efforts, and improving accuracy. Discover how vision AI strengthens automated testing for visually complex applications. This tutorial shows you how to enhance object recognition in SmartBear TestComplete and eliminate test failures caused by 3D applications, canvas-based apps, and virtualized environments.

Learn more: speedscale.com.

Hey, did you know? 62% of government executives say data privacy and security concerns are blocking AI adoption. That’s why Cloudera’s Hilary Billingslea shares why private AI is becoming essential for defense agencies in Cyber Defense Magazine’s 2026 RSA edition.

Discover how the new Lenses 6.2 VS Code plugin revolutionizes the way developers interact with Kafka and streaming data infrastructure. In this deep-dive tutorial, we explore how to manage your entire streaming ecosystem directly from your IDE, eliminating context switching and boosting productivity.

Stop overpaying for AI reasoning by trading expensive GPU cycles for efficient, deterministic testing. This video explores how tools like linters and traffic replay can complement Claude, helping you fix bugs more accurately while cutting token usage by up to 50%. Visit: speedscale.com to learn more.

Check out: https://www.implicator.ai/anthropic-shifts-enterprise-billing-to-per-token-pricing-the-flat-fee-era-is-over/

Or visit: https://speedscale.com/blog/local-llms-on-mac-cost-savings/

As AI agents begin interacting with APIs, traditional API governance models need to evolve. In this DeveloperWeek session, Derric Gilling (WSO2) explains how organizations can manage and secure agent-driven API consumption using the Model Context Protocol (MCP). Unlike human applications, AI agents can generate large volumes of API calls from a single prompt. Without proper controls, this can lead to unexpected costs, security risks, and limited visibility into how APIs are being used.