Systems | Development | Analytics | API | Testing

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.

Release day shouldn't mean chasing answers across Jira. SmartBear Zephyr is the Jira-native testing system of record that empowers your team to deliver better software, faster. In this demo, see how Zephyr Skills for Rovo bring test management and automation insights directly into Jira. Connect planning, testing, and delivery in a single, unified workflow within the Atlassian system of work so your team can make faster, more confident release decisions.

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.

In this final video of our enterprise AI security series, we cover ClearML's monitoring and audit trail capabilities — the visibility layer that ties everything together. We walk through the platform's operational dashboards, task-level audit surfaces, cost attribution, and external integration points, showing how ClearML delivers live operations and compliance-ready audit out of the box.