From AI Prototype to Production : What Happens After the AI Prototype Works?
About this session: Building an AI prototype is becoming easier. With today's LLMs, APIs, frameworks, and AI development tools, engineers can move from an idea to a working demo faster than ever.
But getting a prototype to work is only the beginning.
The real engineering challenge starts when that prototype needs to become a reliable application that real users can depend on. Suddenly, questions around deployment, data quality, evaluation, monitoring, security, scalability, performance, and cost become just as important as the model itself.
This webinar explores what happens after an AI prototype works and what engineers need to consider when taking an AI application into production. Through practical examples and a guided walkthrough, participants will see how engineers think about reliability, architecture, deployment, evaluation, and ongoing maintenance when building real-world AI systems.
The session also introduces Moringa School's Applied AI Engineering programme, a hands-on 14-week programme designed for software engineers and data scientists who want to move beyond AI prototypes and develop the skills to build, deploy, and scale production-grade AI applications.
Objectives:
Help engineers understand the difference between an AI prototype and a production-ready AI application.
Introduce the key engineering considerations that emerge when moving AI systems into production.
Demonstrate how deployment, evaluation, monitoring, reliability, and scalability fit into the AI development lifecycle.
Give participants a practical view of what AI Engineers actually work on beyond model development.
Position Moringa School as a practical, industry-focused provider of AI Engineering training.
Generate qualified leads for the Applied AI Engineering programme.