Run AI in production
like it's just another
workload.
Treeja tech breaks down AIOps and MLOps the way they actually get used in production — monitoring, automation, model serving, and the systems that keep AI workloads running reliably at scale.
What you'll actually learn
No trend-chasing — just the parts of the stack you'll be operating, debugging, and explaining to your team.
AIOps live
Anomaly detection, alert noise reduction, and root-cause analysis — practical ways to bring ML into monitoring and incident response pipelines.
MLOps live
Pipeline orchestration, model versioning, and rollout strategies — the practices that take a model from notebook to a reliable production service.
LLMOps building
Serving, evaluating, and monitoring large language models in production — prompt pipelines, cost control, and guardrails that hold up at scale.
Future Tech queued
Early looks at what's coming next for AI operations — agentic systems, AI-native tooling, and the ideas worth watching before they're mainstream.
Who's behind this
Treeja tech is focused on one thing: how AI and ML systems are actually run in production — the monitoring, automation, and operational practices that keep them reliable once they leave the notebook.
- AIOps & intelligent monitoring
- MLOps pipelines & model serving
- Model monitoring & drift
- LLMOps & AI-native tooling
Don't miss the first episode
New videos are queued and on the way. Follow along on any platform — they all feed straight into the same roadmap above.
Have a question or an idea?
For collaborations, topic requests, or anything else — reach out directly by email.