Productionising Large Language Models

Turn large language models into production-ready systems

Large language models (LLMs) are powerful, but moving them from development into production brings unique challenges. This online course shows you how to operationalise LLMs with the tools, frameworks, and strategies needed for scalable, enterprise-grade deployment.

You’ll start by building strong foundations in production AI, then progress to hands-on model development and deployment pipelines, and finish by exploring the trends that will shape the future of LLMs in industry.

Build strong foundations for production AI

Begin with the essentials of deploying AI at scale. You’ll examine how LLM production differs from classic machine learning, while developing skills in prompt engineering, cost management, and data curation.

You’ll also explore model selection, fine-tuning, and the infrastructure needed for reliable and secure enterprise systems.

Develop and deploy production pipelines

Next, put your learning into practice. Gain experience with fine-tuning workflows and evaluation strategies for custom domains.

Build robust pipelines that include monitoring, maintenance, and automated deployment. You’ll apply best practices for scaling, reducing latency, and managing production environments with observability and drift detection tools.

Explore future trends and enterprise impact

Finish by looking ahead to where LLM production is heading. You’ll discover emerging approaches such as multimodal models and green AI, and understand the safety guardrails that support responsible use, including bias mitigation, security compliance, and resource management.

By the end of the course, you’ll be ready to move large language models from prototypes to production with confidence, supporting scalable, secure, and adaptable AI systems.

This course is ideal for machine learning, DevOps, and platform engineers, as well as architects working with large language models and enterprise GenAI solutions.

This course is ideal for machine learning, DevOps, and platform engineers, as well as architects working with large language models and enterprise GenAI solutions.

• Execute advanced fine-tuning workflows including LoRA optimization and domain-specific model customization.
• Implement enterprise-scale deployment strategies with containerization, monitoring, and infrastructure automation.
• Construct comprehensive production monitoring and maintenance protocols with automated alerting and performance tracking.
• Apply advanced optimization techniques including caching, edge deployment, hybrid routing, and emerging technology adoption strategies.