Systems | Development | Analytics | API | Testing

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.

In this video, we dive into how Click Data Products combine Data Quality, Semantic Types, and business context to create trusted, scalable, and reusable data assets. We explore how semantic types in Qlik help classify and validate data using meaningful business definitions — improving consistency, discoverability, and confidence in analytics and AI-driven insights.

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.

For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.