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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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zymr

Building Compliant Banking Platforms in a Multi-Cloud Environment: Architecture, Risks & Best Practices

May 5, 2026 By Haresh Kumbhani In Zymr
Banks are under pressure. Not just to innovate, but to do it safely. Customers expect seamless digital experiences. Regulators expect absolute control. And somewhere in between, banks are trying to modernize systems that were never designed for this level of speed or scrutiny. This is where Compliant Banking Platforms come into play. Today, financial firms have already embraced hybrid or multi-cloud strategies to balance costs and meet stringent compliance requirements.
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hitachi vantara

Turning Virtualization Modernization Into Business Outcomes

May 5, 2026 By Dinesh Singh In Hitachi Vantara
As enterprises navigate rising virtualization costs and increasing infrastructure complexity, many are rethinking their approach to modernization. One organization leading this transformation is Alior Bank, a forward-looking financial institution that successfully modernized its IT environment to improve agility, resilience, and cost efficiency.
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dreamfactory

Multi-Version API Management for AI Workflows | DreamFactory

May 5, 2026 By Kevin Hood In DreamFactory
Last Updated: May 2026 Asking the right questions when building an API for AI systems is critical, especially when updates risk breaking existing integrations. Here's the deal: API versioning ensures your AI workflows stay stable while introducing new features. By supporting multiple API versions, you can test updates, maintain compatibility, and avoid disruptions.
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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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InsightSoftware

How to Connect Power BI to Amazon DataZone (Without a JDBC Bridge)

May 5, 2026 By InsightSoftware In InsightSoftware
Amazon DataZone is a powerful data management service that lets teams catalog, discover, and govern data across AWS environments. But when it comes to connecting your BI tools, options are limited. Data teams trying to connect Power BI to Amazon Datazone often hit the same wall when every guide, forum thread, and AWS doc points you toward a JDBC bridge or driver. However, Power BI doesn’t speak JDBC natively, which quietly costs data teams time, stability, and patience.
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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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