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Multi-agent systems aren't new architecture—they're microservices evolved. Varun Jasti of AWS explains why Apache Kafka is the natural backbone for agent-to-agent communication at scale.
Stop your AI projects from being abandoned due to a lack of data readiness. Cloudera AI provides the tools to secure, govern, and prepare your data for production, no matter where it lives. Turbocharge your AI journey today. Contact your Cloudera representative to learn more. *Read More:* Check out our blog post on solving the AI backlog.
When Substack first launched in 2017, the company set out to give writers a better business model, built on subscriptions and direct relationships with readers. Since then, Substack has expanded into multi-format publishing across text, audio, and video, while building powerful tools for community and discovery, for creators, writers, and thinkers of all kinds.
ClearML Enterprise v3.29 builds on the governance and infrastructure foundations introduced in recent releases. This update focuses on giving administrators and AI teams more granular control over resource allocation, gateway access, and pipeline management while delivering a meaningful set of UI quality improvements across the platform.
For decades, SME lending has lived in a strange space. On one hand, small and medium enterprises are the backbone of every economy. They drive employment, fuel innovation, and keep local markets alive. On the other hand, getting access to credit has always been frustratingly difficult for them. Why? Because traditional lending systems were never designed for them. Banks relied heavily on collateral, long credit histories, and static financial statements.
Most AI failures aren’t model problems. They’re data pipeline problems. Disconnected systems. Inconsistent preparation. No governance at query time. This short animation walks through the 5 Pillars of AI-Ready Data and shows how data needs to move through a structured pipeline before it can power reliable AI. 5 Pillars of AI-Ready Data Access → Prep → Context → Governance → Monitoring Five stages. One connected flow.
Is your AI agent one misconfigured server away from a production data leak? In this deep dive, Jeremy from Lenses explores the critical security architecture of the Model Context Protocol (MCP) and how it’s evolving to protect the future of Agentic Engineering.
Discover the powerful new IDE-like Studio in Lenses 6.2. Learn how to manage your Kafka clusters, discover topics across multiple environments, and perform side-by-side comparisons of dev and staging data. We also dive into the new ways to interact with streaming data, including the CLI, VS Code plugin, and the new MCP server for AI agents and chatbots. Whether you're a developer troubleshooting schema mismatches or a data engineer managing complex Kafka estates, the new Lenses Studio provides the tools you need to stay in context and work efficiently.
Where agentic AI meets your analytics stack to drive action at scale. The shift is here. As the industry moves from Generative AI (Chat) to Agentic AI (Action), the pressure is on for developers and data practitioners to design intelligent apps that don't just talk: they perform. The real challenge? Bridging the gap between sophisticated developer tooling and your enterprise analytics stack. That’s exactly what this session solves.