Enterprise Generative AI Training

Enterprise Generative AI: Architecting & Scaling Production-Ready AI Systems

About the Course: Deploying Generative AI at an enterprise scale requires more than passing prompts to an API. It demands robust data pipelines, highly efficient retrieval systems, and bulletproof infrastructure.

Led by technology expert Anthony Robinson, this 24-hour technical deep dive shifts the focus from high-level business roadmaps directly into the engineering reality of modern AI systems. This course breaks down the underlying technical mechanics of Large Language Models (LLMs), advanced Retrieval-Augmented Generation (RAG), and complex agentic workflows.

Through practical architectural frameworks, systems engineering principles, and infrastructure breakdowns, you will learn how to design, secure, and monitor high-throughput, low-latency AI ecosystems built for heavy enterprise workloads.

What You Will Learn:

• Systems Architecture: Engineering the core data pipelines and orchestrations required for massive LLM deployments.

• Advanced Data Processing: Structuring enterprise knowledge bases using optimized vector databases, chunking strategy, and intelligent retrieval.

• Infrastructure & Optimization: Techniques for handling high concurrency, managing API latency, and optimizing compute/token expenditures.

• Technical Governance: Setting up continuous validation, prompt version control, model drift tracking, and automated security guardrails.

Who This Is For:

• Software Engineers, Solutions Architects, and Data Specialists tasked with building AI systems.

• Technical Leaders looking to master the technical stack behind LLMs and RAG.

• DevOps and MLOps engineering teams looking to stabilize and scale AI models in production environments.