Systems | Development | Analytics | API | Testing

How to Make the Most of AI Tools for Modernization

AI tools promise speed — but what does AI modernization actually mean in practice? In this video, learn how the best AI tools can accelerate application modernization without increasing risk. We cover how AI tools analyze large legacy codebases, support refactoring, and speed up modernization—when paired with expert human oversight. You’ll learn: Whether you’re exploring AI tools or already modernizing, this video shows how combining AI acceleration with experienced engineers leads to better outcomes.

PHP Debugging: How to Find and Fix PHP Errors

PHP applications are often tricky to debug. A combination of loose typing, complex logic and a lack of runtime visibility can make it hard to catch errors before they reach our users. But if you’re using PHP, there’s no need to stress. This guide will equip you to understand why PHP applications break, return the wrong data or behave differently across environments. We’ll cover logs, runtime checks, Xdebug, IDE tools, request debugging, and production visibility.

Establishing a Multicloud Data Strategy for the AI Era

In my experience working with enterprise leaders, the journey to the cloud rarely follows a straight line. Many organizations set ambitious goals to move all operations to the cloud. They quickly find that certain legacy systems must remain on-premises. This reality results in a complex, hybrid multicloud environment. That means they need to adopt a new strategy for managing test data.

How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost

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.

Enterprise AI Infrastructure Security Series - 7) Monitoring & Auditing

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.

Why RBAC Isn't Enough: Real Tenant Isolation in Kubernetes AI Environments

Role-based access control is essential, but it’s not isolation. When multiple AI teams share a Kubernetes cluster, RBAC controls what they can do; it doesn’t control what they can reach, what they can see, or what happens when something goes wrong in a neighboring workload. This is the first post in our four-part series on Kubernetes Security for Enterprise AI Environments.

From Kafka Chaos to Control: A Practical Guide to Governing Real-Time Data

Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.