LLMOps and Large Language Model Operations Training Course

LLMOps and Large Language Model Operations Training Course

Introduction

LLMOps and Large Language Model Operations Training Course is designed to equip professionals with advanced skills required to deploy, manage, monitor, optimize, and scale Large Language Models (LLMs) in real-world enterprise environments. As organizations rapidly adopt Generative AI, Foundation Models, AI Agents, Retrieval-Augmented Generation (RAG), and Enterprise AI Platforms, the demand for professionals who can operationalize AI systems has grown significantly. This course focuses on the complete LLM lifecycle management, including model deployment, MLOps integration, prompt management, model governance, performance monitoring, AI infrastructure automation, security, and responsible AI practices.

Participants will gain practical expertise in building robust LLMOps pipelines using modern AI engineering frameworks and cloud-native technologies. Through hands-on labs, industry case studies, and real-world scenarios, learners will explore LLM versioning, model evaluation, inference optimization, observability, cost management, fine-tuning operations, API management, scalable AI architecture, and continuous improvement workflows. The course prepares AI engineers, data scientists, DevOps professionals, and technology leaders to successfully operate production-grade LLM applications in modern digital enterprises.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand the fundamentals of LLMOps, MLOps, and AI lifecycle management. 
2. Design and implement enterprise-scale LLM deployment pipelines. 
3. Apply continuous integration and continuous deployment (CI/CD) practices for AI systems. 
4. Manage LLM version control, model registry, and artifact tracking. 
5. Build scalable Generative AI infrastructure and cloud-native AI platforms. 
6. Implement LLM monitoring, observability, and performance optimization strategies. 
7. Apply prompt engineering operations and prompt lifecycle management. 
8. Develop efficient RAG operations and knowledge management workflows. 
9. Optimize LLM inference performance, latency, and operational costs. 
10. Implement AI security, privacy, compliance, and responsible AI governance. 
11. Automate LLM workflows using DevOps and automation frameworks. 
12. Evaluate and benchmark LLM quality, accuracy, and reliability metrics. 
13. Build production-ready AI applications using modern LLMOps best practices. 

Target Audience

1. AI Engineers and Machine Learning Engineers 
2. Data Scientists and Data Analysts 
3. MLOps and DevOps Engineers 
4. Cloud Architects and Solution Architects 
5. Software Developers building Generative AI applications 
6. AI Product Managers and Technology Leaders 
7. Enterprise Digital Transformation Professionals 
8. Research Scientists working with Foundation Models 

Course Modules

Module 1: Introduction to LLMOps and AI Operations

• Understanding the evolution from MLOps to LLMOps
• LLM lifecycle management and operational challenges 
• Generative AI architecture and enterprise AI workflows 
• LLMOps roles, responsibilities, and best practices 
• Building an LLMOps strategy for organizations 
• Case Study: Implementing an enterprise LLM platform for a global customer support organization using automated AI operations.

Module 2: Large Language Model Deployment Architecture

• Designing scalable LLM deployment architectures 
• Cloud-based and on-premise LLM infrastructure 
• Containerization using Docker and Kubernetes 
• API-based LLM serving architectures 
• Managing foundation model deployments 
• Case Study: Deploying a financial institution’s AI assistant using scalable LLM APIs and Kubernetes infrastructure.

Module 3: LLM Development Lifecycle Management

• LLM experimentation and development workflows 
• Model versioning and artifact management 
• Dataset management and AI pipeline tracking 
• Managing fine-tuned model releases 
• Reproducibility and automation in LLM projects 
• Case Study: Managing multiple versions of a healthcare AI model while maintaining compliance and reliability.

Module 4: CI/CD and Automation for LLM Applications

• Building AI-focused CI/CD pipelines 
• Automated testing for LLM applications 
• Deployment automation strategies 
• Infrastructure as Code for AI environments 
• Continuous improvement workflows 
• Case Study: Creating an automated deployment pipeline for a GenAI enterprise chatbot.

Module 5: LLM Monitoring, Observability, and Optimization

• Monitoring LLM application performance 
• Tracking latency, throughput, and resource usage 
• AI quality monitoring and evaluation metrics 
• Detecting model drift and performance degradation 
• Cost optimization for large-scale inference 
• Case Study: Optimizing an AI search platform by reducing inference costs while maintaining response quality.

Module 6: PromptOps and Retrieval-Augmented Generation Operations

• Managing prompt engineering workflows 
• Prompt version control and testing 
• RAG pipeline deployment and monitoring 
• Vector database operations 
• Knowledge retrieval optimization 
• Case Study: Operating an enterprise knowledge assistant using RAG and automated prompt management.

Module 7: LLM Security, Governance, and Responsible AI

• Securing LLM applications and APIs 
• Preventing prompt injection and AI misuse 
• Data privacy and compliance management 
• Responsible AI governance frameworks 
• Model risk management 
• Case Study: Implementing secure AI governance for a banking organization deploying internal LLM applications.

Module 8: Advanced LLMOps Platforms and Future AI Operations

• Building autonomous AI operations workflows 
• AI agents and agentic system management 
• Multi-model orchestration strategies 
• Enterprise AI platform architecture 
• Future trends in LLMOps and AI engineering 
• Case Study: Designing an enterprise AI ecosystem combining multiple LLMs, AI agents, and automated operations.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you