Systems | Development | Analytics | API | Testing

What happens when your AI system stops responding in the middle of a critical decision? This demo shows how organizations run AI inference for real-world applications like pneumonia detection to: See how Cloudera AI Inference Service enables teams deploy and monitor multiple models with full control, predictable costs, and no dependency on external APIs, so mission-critical AI keeps working when it matters most.

Human spaceflight is one of the few domains in which data and human judgment must work together flawlessly under extreme pressure. That makes it a powerful lens for understanding what it takes to build resilient, intelligent systems here on Earth. In this Women Leaders in Technology spotlight episode of The AI Forecast, Paul Muller sits down with former NASA astronaut Dr. Jeanette Epps to explore what complex, high-stakes environments can teach us about AI.

Hey, did you know AI can’t be confined to just one environment? AI is moving faster than ever, but it cannot be confined to a single environment. From the public cloud to on-prem data centers and out to the edge, AI is everywhere. However, when these environments remain siloed, your data strategy breaks—leading to inconsistent governance and scaling roadblocks. In this video, discover the vision of AI Anywhere. To unlock real business value, AI must operate exactly where your data lives, maintaining the same level of control, trust, and security across every platform.

Have you ever wondered who keeps the world’s biggest networks running smoothly? Nine out of 10 top global telcos trust Cloudera to handle the heavy lifting. From processing a staggering 10 million events per second to managing data across the globe—from Indonesia to Africa—Cloudera provides the hybrid scale and "cloud anywhere" flexibility that massive networks need to stay secure and compliant. It’s all about delivering top-tier network quality and the best customer experiences through end-to-end governed data and AI.

Building private AI doesn't have to be expensive or complicated. In this video, Mason Jung from Cloudera demonstrates how to post-train a language model directly on your own device.

In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data trust and integrity. In this episode of The AI Forecast, Paul Muller sits down with The Data Governance Coach, Nicola Askham, to share her pragmatic perspective and assert that governance only delivers value when it’s simple enough for people to use and embedded into day-to-day work.

Most AI strategies aren't failing because of models—they’re failing because data is fragmented, siloed, and hard to access. In fact, nearly 8 and 10 organizations say incomplete data access is holding them back. Moving the data drives up cost, introduces latency, and increases compliancy and security risks. Cloudera has introduced the Workflow Data Fabric Zero Copy Connector for ServiceNow to solve this. It allows you to securely leverage nearly 30 exabytes of data under management to power agented workflows without moving the data from wherever it lives.

Post-training is rapidly becoming a critical phase of enterprise AI development. To get reliable output from an AI model, organizations must align its terminology (e.g., abbreviation) to fit their specific use cases. But getting started shouldn't require heavy computing resources—you can quickly train an open-source model right on your local device. In this tutorial, we sit down with the ASAP_DPO_Finetuning Cloudera AMP to demonstrate exactly how to align a language model to specific industry standards—in this case, Oil & Gas abbreviations.

Discover the importance of optimization when operationalizing a data lakehouse for production workloads. We break down the journey of bringing a lakehouse into production—from choosing your data file format (Parquet) and table format (Iceberg) to plugging in your catalog and compute engines. Finally, learn why balancing ingestion jobs with critical table management services makes all the difference when moving beyond single-node workloads.