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ClearML

Resource Governance and GPU Quota Enforcement Across AI Teams

May 6, 2026 By Adam Wolf In ClearML
Resource governance is primarily an operational discipline, but it has direct security implications that are usually overlooked. This post covers what those implications are, what Kubernetes provides natively, where it falls short for AI workloads, and how ClearML addresses both dimensions. This is the third post in our four-part series on Kubernetes Security for Enterprise AI Environments.
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katalon

Quality People: From Scripts to Harnesses, the Evolution of Agentic QA

May 6, 2026 By Joe Sturgess In Katalon
Play Quality People - Huy Tieu: The Evolution of Agentic QA: From scripts to Harnesses 17: 18 A conversation with Huy Tieu, Senior Product Manager at Katalon, on why the scripted testing model broke, what replaces it, and the one experiment every QA leader should run this week. The term "Agentic QA" is everywhere in 2026. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year's end, up from under 5% in 2025.
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Scaling AI with Trust: Real-Time Access to Governed Data

May 6, 2026 By Cloudera, Inc. In Cloudera
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.
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ClearML

Secrets, Credentials, and the Kubernetes Attack Surface in AI Environments

May 5, 2026 By Adam Wolf In ClearML
Every AI workload needs credentials: cloud storage keys, model registry tokens, database passwords, and API keys for external services. How those credentials are managed in Kubernetes determines whether they stay secret or become the entry point for a serious breach. ClearML Vaults addresses this directly by separating credential ownership from credential use at the platform level. This is the second post in our four-part series on Kubernetes Security for Enterprise AI Environments.
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confluent

Why Real-Time Stream Processing Beats Batch ETL for AI Data Freshness in 2026

May 5, 2026 By Manveer Chawla In Confluent
AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.
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confluent

Integrating AI Into Apache Kafka Architectures: Patterns and Best Practices

May 5, 2026 By Manveer Chawla In Confluent
Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes down to one thing: picking the right boundary between data transport and model compute. Where you run inference determines your system's resilience, latency, and cost. This article is for data engineers, streaming architects, and developers who want to add AI capabilities to their Apache Kafka event backbone without destabilizing production consumer groups or blowing through API rate limits.
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New Zephyr Skills for Rovo: AI-powered test management in Jira | Zephyr

May 5, 2026 By SmartBear In SmartBear
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.
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AI post-training: Finetuning using PEFT and DPO on Cloudera AMP

May 5, 2026 By Cloudera, Inc. In Cloudera
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.
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