Operations | Monitoring | ITSM | DevOps | Cloud

Ship Reliable AI Faster: How to Operate AI Agents with Control and Confidence

Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones.

From Legacy to AI-Ops: Securing and Scaling Systems for 20M Device Requests with Datadog

Modernizing a legacy system serving 20 million devices without users noticing is like replacing a jet engine mid-flight. In this session, YoungJin Jung and Donggen Hong from LG U+ share their 18-month journey transforming a Telco-scale API Gateway from a rigid, proprietary solution into a high-performance, open-source architecture on AWS, and the operational challenges they solved along the way.

Why Is Root Cause Analysis So Hard for IT Teams to Get Right?

In this video, learn what Root Cause Analysis (RCA) is and why it's essential for preventing recurring IT incidents instead of repeatedly fixing the same symptoms. Discover how effective RCA helps IT teams identify the real source of problems, reduce downtime, and improve operational resilience. In this video, you'll learn: Contact Us sales@motadata.com Resources Follow Us on Social Media.

Streamline & Automate Work with New Features on the Velocity Platform

As your business evolves in an ever-changing economy, your operations team has the chance to drive innovation and unlock new efficiencies. Watch an expert-led demo showcasing the newest features on the Velocity Platform — designed to empower your organization to accomplish better work, faster. In this interactive session, you'll: Explore the latest Velocity Platform updates that work for you. See why Velocity Web is far more than just a browser.

Language AI to physical AI explained

What is physical AI? Physical AI embeds machine learning directly into hardware, enabling algorithms to interact, move, and perform autonomous tasks in the physical world. Traditionally, robots relied on precise, hardcoded coordinates; if an object shifted by a single millimeter, the entire system failed. Today, robotics is moving past rigid automation toward truly adaptive architecture. Neural networks help machines process raw sensor data in real time. Consequently, machines can dynamically reason through the unpredictable physical world.