MLOps, LLMOps & AI Observability Training in Astana, Kazakhstan

This course provides hands-on and conceptual understanding of deploying, managing, and monitoring machine learning (ML) and large language model (LLM) applications in production. Participants will learn about MLOps pipelines, LLMOps principles, and observability techniques to ensure reliability, traceability, and performance of AI systems.

MLOps Foundations

• What is MLOps: Concepts & Use Cases

• Overview of ML lifecycle vs. software lifecycle

• MLOps tools landscape (MLflow, DVC, Airflow, Kubeflow)

• CI/CD for ML (GitHub Actions, Jenkins)

MLOps in Practice

• Building end-to-end ML pipelines

• Model validation, testing & packaging

• Containerizing ML models (Docker basics)

• Deployment patterns: Batch vs Real-time vs Streaming

LLMOps

• Introduction to LLMs (BERT, GPT, LLaMA, etc.)

• Unique challenges with LLMs (prompt management, evaluation, fine-tuning)

• LLMOps lifecycle: Selection, Deployment, Prompt Ops, Monitoring

• LLM CI/CD strategies

AI Observability

• What is AI Observability and why it matters

• Key metrics: Model performance, data drift, concept drift

• Logging, tracing, and alerting in ML/LLM systems

Advanced Monitoring, Governance & Real-World Projects

• Model governance & lineage tracking

• Responsible AI and compliance (bias, fairness, auditability)

• Troubleshooting models in production

• Multi-model orchestration and cost tracking

Hands-On Labs

Summary and Conclusion