Transformer Architecture and Large Language Models in Azure Machine Learning

The rise of large language models has transformed the landscape of artificial intelligence, yet understanding the underlying architecture is key to leveraging their true power. This text-based course guides you through the core mechanics of modern natural language processing and shows you how to work with these models in the cloud. You will transition from understanding basic language processing history to conceptualizing the inner workings of the Transformer architecture. By the end of this course, you will know how to navigate the model catalog in Azure Machine Learning to select, deploy, and interact with state-of-the-art foundation models. What you will learn: Understand the evolution of natural language processing and the breakthrough of attention mechanisms; Demystify the encoder-decoder structure of the Transformer architecture; Explore the foundational concepts of large language models and their capabilities; Navigate the Azure Machine Learning model catalog to discover pre-trained foundation models; Deploy and test models within Azure Machine Learning for text generation tasks; Apply modern prompt engineering basics and conceptualize retrieval-augmented generation patterns. The course begins with foundational definitions and the history of language processing before diving deep into attention mechanisms. From there, you will transition to practical cloud-based workflows, learning how to manage and deploy models in the Azure environment. This training is designed for aspiring data scientists, cloud developers, and AI enthusiasts who are new to deep learning architectures and cloud-based machine learning environments, with no prior experience required. Start reading today to unlock the potential of modern language models in the cloud.