MLOps/LLMOps career mentoring | Roadmap | Growth

Turn ML and LLM projects into systems that survive production.

Service Description

Built models but don’t know how they actually run in production?

This session bridges the gap between experiments and reliable ML/LLM systems.

I’ll help you think like an engineer who owns ML in prod — pipelines, failures, scale, and costs included.

What You’ll Get

• End-to-end MLOps / LLMOps architecture guidance

• How to productionize notebooks into real services

• Model deployment, versioning, monitoring & rollback strategies

• LLMOps patterns: prompts, evals, latency, cost control

• CI/CD for ML pipelines

• Tooling clarity (MLflow, Airflow, Kubeflow, cloud-native stacks)

• Career guidance for MLOps / LLMOps roles

Who It’s For

• ML engineers struggling with production

• Backend/DevOps engineers moving into ML systems

• Engineers working with LLMs beyond demos

• Teams or individuals shipping ML for the first time

Outcome

Confidence to design, deploy, and operate ML/LLM systems in real environments.