Agenda

Moving sensitive data to public AI models is a costly, risky compromise. The true enterprise differentiator lies in scaling trusted, autonomous agents for complex workflows—without sacrificing privacy or succumbing to public cloud "token traps." Discover how Cloudera achieves sovereign AI by bringing high-performance AI directly to your data, whether in hybrid clouds, private infrastructure, or air-gapped data centers.

What You Will Learn:

• Private, Cost-Predictable Model Serving: Securely host models behind your firewall with Cloudera AI Inference service, lowering TCO compared to public APIs.
• Fast-Tracked Agentic Innovation: Rapidly scale autonomous workflows with low-code Cloudera Agent Studio, ensuring safe interaction with production systems.
• Centralized AI Governance: Manage, monitor, and route AI usage via a unified control plane that eliminates "shadow AI" and ensures strict compliance.

Join us to see how a data-to-AI strategy minimizes risk, lowers TCO, maximizes operational sovereignty, and accelerates production-grade enterprise intelligence.

Demands for AI are surging at the edge and across the globe as enterprises rush to deploy autonomous agents. However, scaling these intelligent workloads introduces complexities in compliance and infrastructure, because moving massive volumes of sensitive operational data to centralized public clouds triggers strict regional data sovereignty risks and cost-prohibitive "token traps." 

Hear how your fellow data leaders are managing the balancing act of delivering AI anywhere by bringing high-performance model serving to their local data infrastructure. Join to learn how by shifting your compute to the data instead of relying on risky, expensive public APIs, you can eliminate "shadow AI" and maximize operational sovereignty while maintaining strict, uncompromised control over corporate privacy, regulatory compliance, and predictable TCO.

Hear insights from:

• Lily Wongso, EVP of Enterprise IT Architecture, Data Management and Service Quality Group, Bank Central Asia

• Billie Setiawan, CDAO, Head of Data Analytics & AI, CIMB Indonesia

The next frontier of data infrastructure is not just a lakehouse that stores more data — it is a lakehouse that understands more, heals itself, and federates context across every boundary to feed your agents. In this session, learn the three pillars of the intelligent lakehouse and how they work together to deliver a complete, governed, 360-degree context layer that makes AI agents more reliable.

With multimodal intelligence — you’ll see how to parse, extract, and vectorize unstructured data, and unify it with structured data. Take a peek inside the agentic lakehouse — how AI-driven optimizations make your platform self-healing, self-tuning, and continuously performant. And finally, learn how Apache Iceberg, Trino, and our connectors with Salesforce, ServiceNow, and SAP power true interoperability and federated access for context 360. Discover data anywhere, access data from anywhere, share data products, and power your agents anywhere in the cloud, at the edge, or even on premises, giving you complete freedom to choose the architectures, tools, models, and applications that fit your business today and in the future.

Every organization is looking to bridge the gap from insight to action. Their success hinges on a single factor - delivering the right business context to the AI and agents in a secure and protected manner to automate their workflows.

Context goes beyond data. It also includes the metadata, business semantics, relationships,  governance, security policies, and lineage. Agents need context to understand the data and use it securely to generate reliable, trustworthy outcomes. Cloudera is building a unified data fabric that delivers the context layer that your agents need. 

Hear insights from:

• Tushar Garg, Lead Data Engineer, IDFC First Bank

• Achal Ananth, VP & Head of Data - Analytics, BI, Data Science & Data Platform, PhonePe

• Mohd Marzuq Ikram bin Mohd Helmi, Vice President, Head of Analytics Engineering, Group Data & Analytics, RHB Bank

Deriving real value from your data estate -- from acquiring and preparing data to actively extracting insights -- remains a painstaking hurdle. Because basic chat tools fall short of handling this complexity, it’s imperative to close the loop between a user’s intent and actual work that leads to the desired outcome. An agentic copilot experience, an intelligent layer embedded directly into your workflow, brings together specialized agents, business context, and fit-for-purpose compute engines. Whether you are a pipeline builder, business analyst, or operator, learn how to be more productive and get to results faster.

Hear how a leading manufacturer relied on Cloudera to achieve its goal of delivering a unified cloud experience across public clouds, on-premises, and even at the edge with a fast implementation that covered simplified migration, deployment, LTS, authentication, authorization, and cloud deployment.

This session shows how financial data moves from raw ingestion to governed, AI-ready consumption on an Iceberg-based lakehouse. Using a real-world finance scenario, we will show you how to transform data into trusted tables, enrich the data, validate its quality, and make the data usable for analytics, AI assistants, and responsible decisioning workflows.

The Cloudera data and AI platform, powered by AMD compute, enables enterprises to deliver cost-effective generative and agentic AI use cases with the flexibility to deploy scalable, secure AI across public clouds, data centers, and the edge. Learn how AMD and Cloudera together help organizations deploy hybrid private AI with the governance, data management, security, and performance needed to scale AI workloads efficiently.

In the era of rapid AI adoption, organizations are constrained by the rising costs of public models with unpredictable token fees that make scaling cost-prohibitive. They are also bottlenecked by emerging complexities in data movement, incurring high cloud egress fees and severe latency when shifting sensitive assets. What’s more, organizations need to maintain compliance requirements driven by strict data sovereignty laws that forbid exposing corporate IP to public APIs.

Discover how Axis Bank  is  navigating these challenges with new architectures, tools, and capabilities that bring AI directly to their data–slashing TCO, eliminating compliance risks, and safely fast-tracking production-grade innovation.

We are living through the most consequential transition in enterprise technology — not just in how we use data, but in what we expect it to do to improve business. The era of insight is giving way to the era of actions and automation and data has been the cornerstone of this transition. In this session, Cloudera customers share their fundamental shifts in strategy  as well as their adoption of key data and AI capabilities to modernize for improved business outcomes.

Hear insights from:

• Sanjay Kumar, Senior Vice President-Head Data Platform & CDO, Axis Bank

• Sushil Ostwal, Vice President (Head of Data Warehouse), Bombay Stock Exchange

Data movement makes data vulnerable. Ensure that only the right people and the right applications can get access to your data, with Protegrity. Learn how Protegrity and Cloudera work together to protect your data, on premises, in the clouds, wherever it is located, for complete assurance that your data remains protected anywhere.

Learn how Cloudera AI Inference Service can enable you to run LLMs on your own infrastructure, bringing the power of open-source models into fully private, on-premises and cloud environments without ever sending sensitive data to a third party. You'll see how Cloudera handles the operational complexity — from model serving and autoscaling to observability — so your teams can focus on building Agents–not managing infrastructure. But Private AI is only part of the picture. Experience how Cloudera's new AI Gateway gives you a single control plane to route requests to both private and public models, enforce policies, track usage, and manage costs — all without fragmenting your AI strategy.

Explore how to design and implement an agentic workflow from end to end. We will walk through a real use case, show how the agent creates its own to-do list and plans the work, keeps execution within guardrails, and leaves a complete audit trail along the way. Expect a hands-on, practical look at how governed AI agents actually come together.