Large language models (LLMs) like GPT-4 have revolutionized the field of natural language processing (NLP) with their ability to generate human-like text and perform various tasks based on the input provided. However, to fully unlock the potential of these pre-trained models, it is essential to streamline the deployment and management of these models for real-world applications. This session will guide you through the process of operationalizing LLMs, including prompt engineering and tuning, fine-tuning, and deployment, as well as the benefits and challenges associated with this new paradigm.

You will learn how to:
- Access and discover various LLMs from Azure OpenAI Service, Hugging Face, and other sources using the Azure Machine Learning model catalog.
- Tune the prompts and fine-tune the models for domain-specific grounding using Azure Machine Learning prompt flow and advanced optimization technologies.
- Deploy the models and prompt flows as scalable and secure endpoints using Azure Machine Learning Studio and SDK.
- Monitor the deployed models and prompt flows for data drift, model performance, groundedness, token consumption, and infrastructure performance using Azure Machine Learning model monitoring.
- Apply responsible AI principles and best practices to ensure ethical and compliant use of LLMs.

Whether you are a seasoned AI practitioner or a beginner looking to expand your knowledge, this session will equip you with valuable insights and skills to operationalize LLMs with Azure Machine Learning. Join us for this exciting journey and discover how LLMOps is shaping the future of AI and Machine Learning.