Craig Tavares, COO of Buzz High Performance Compute, shares lessons from building GPU cloud infrastructure worldwide. He stresses the role of sovereign mandates, renewable power, and modular cooling in scaling data centers, while highlighting NVIDIA partnerships and orchestration as key to sustaining AI workloads.

Graphs are everywhere, but often misunderstood. This blog busts common myths about knowledge graphs, explains why they’re faster and more flexible than you think, and shows how AI can help build them.

Using NotebookLM’s Video Overview, I turned my MLOps Coding Course from text into a full video series in just two days. What once felt like a month-long grind became a fast and creative process — demonstrating how AI can amplify expertise instead of replacing it.

Russ d'Sa shares how LiveKit went from a small open-source project during the pandemic to powering voice interfaces for giants like OpenAI. He talks about the turning point when LiveKit teamed up on ChatGPT’s voice features, the challenges of making AI sound human, and why voice could be the future of multimodal AI. It’s a story of chance, big shifts, and building the backbone of tomorrow’s AI.

Machine learning pipelines are vulnerable to data and infrastructure errors that can disrupt production. By implementing smoke tests with both random and controlled synthetic data, teams can validate pipeline functionality and schema adherence before running full-scale jobs. This practice supports continuous integration and delivery, leading to fewer outages and more reliable deployments.

For better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation will discuss ways that enterprise scale organizations are addressing these challenges to safely and securely build these development environments.

Building better agents fast: real stories, lean workflows, and practical tips for building trustworthy, human-friendly agents in accounting and beyond.

We share a pragmatic framework for evaluating LLM-powered applications in production. Anchored in high-quality human labels and a calibrated ‘LLM-as-judge’ approach, it turns subjective outputs into consistent, actionable metrics—enabling continuous monitoring, faster iteration, and safer launches at scale. We distill lessons from a year of building and operating this framework at Booking.com, with the aim to make evaluation a core practice in the GenAI development lifecycle.