Mastering MLOps/LLMOps

Service Description

Step beyond experimentation and learn how to build, deploy, and operate real-world AI systems at scale. This intensive MLOps / LLMOps Training is designed for professionals who want to master the full lifecycle of machine learning and large language model applications — from raw data to production-grade AI systems used by thousands (or millions) of users. This is a hands-on, technically demanding program focused on real engineering challenges: deployment failures, model drift, latency constraints, scaling LLM applications, and maintaining reliability in production environments. Participants will design and implement end-to-end AI pipelines, apply LLMOps best practices, and learn how top engineering teams operationalize AI. What You'll Learn - Build end-to-end MLOps pipelines - Deploy and scale LLM-powered applications -Implement CI/CD for machine learning & LLM workflows - Monitor performance, detect drift, and automate retraining - Apply advanced LLMOps techniques (RAG, prompt orchestration, evaluation) - Deploy using cloud-native infrastructure & containers - Implement governance, security, and cost optimization Hands-On Topics Include - Production ML architecture design - LLM deployment strategies - Retrieval-Augmented Generation (RAG) pipelines - Prompt engineering at scale - Model versioning & experiment tracking - Kubernetes & containerized deployments - Monitoring, observability, and alerting - Performance tuning & cost management Who This Training Is For - This program is designed for professionals ready to level up their AI engineering capabilities: - Machine Learning Engineers - AI Engineers - Data Scientists moving to production - DevOps / Platform Engineers - Software Engineers building AI products - Technical Leads & Architects - Training Format Intensive, hands-on labs - Real-world production scenarios - Build deployable AI systems - Production-focused engineering practices Outcome By the end of this training, participants will have the skills and confidence to design, deploy, and operate scalable, production-grade AI and LLM systems — transforming AI prototypes into robust, enterprise-ready solutions.