Deploying Language Models in the AI Foundry Catalog

Selecting the right AI model for your application can feel overwhelming with so many options available. This text-based course guides you through navigating the AI Foundry model catalog to find, deploy, and optimize the perfect model for your needs. You will transition from understanding basic model architectures to confidently deploying and testing large language models. By learning how to evaluate model capabilities and apply modern performance-tuning techniques, you will be ready to integrate robust AI models into your software solutions. What you'll learn: Understand the foundational differences between various open-source and proprietary language models in the catalog; Navigate the AI Foundry portal to compare model benchmarks and select the optimal model for your specific use case; Deploy language models as secure, scalable API endpoints for seamless application integration; Test deployed models using prompt engineering basics to verify output quality and performance; Apply modern evaluation metrics to measure and improve model accuracy and latency. The course begins with core definitions and model selection criteria before moving into hands-on deployment steps, testing methodologies, and performance optimization strategies. You will progress through clear, written explanations and practical scenario-based exercises. This course is designed for beginner developers, cloud enthusiasts, and aspiring AI engineers who want a straightforward, conceptual and practical guide to model deployment without needing advanced machine learning expertise. Start reading today to master the essentials of model deployment and elevate your AI development skills.