• Introduction to LLMs and their operational mechanisms.
• Overview of evaluation metrics in the LLM landscape.
• Key operational disparities between MLOps and LLMOps.
• Current trends and the evolving state of Generative AI.
• A glimpse into the tools and technologies shaping the LLMOps ecosystem.

• Identifying suitable problems for LLM solutions.
• Setting up AWS Sagemaker and AWS Bedrock.
• Leveraging foundation models for diverse real-world tasks.
• Enhancing LLM outputs through prompt engineering.

• Exploring various fine-tuning and optimization techniques.
• Model serving strategies on AWS.
• Hands-on LLM fine-tuning and optimization lab on AWS Sagemaker.

• Understanding AWS Sagemaker features.
• Exploring common AWS services. 
• Sagemaker Feature store and other vector databases.
• Implementing Sagemaker pipelines for seamless training, evaluation, deployment, and real-time monitoring.
• Fully automated re-training and deployment using model evaluation.
• Hands-on session on end-to-end pipeline on Sagemaker.

• Unveiling the internal workings of Kubernetes.
• Deploying LLMs on Kubernetes with auto-scaling capabilities.
• Evaluating LLM performance through real-world load testing.
• Monitoring LLMs performance in production environments.

• Exploring vector databases and embedding techniques.
• Understanding RAG pipelines and their versatile applications.
• Weighing the pros and cons of RAG utilization.
• Hands-on session implementing and enhancing RAG techniques for improved performance and accuracy.

• Grasping governance principles in LLMOps and their significance.
• Best practices for ensuring data security in LLMOps.
• Overview of EU AI Draft Regulations and their implications.
• Addressing common LLM vulnerabilities and strategies for effective guardrails.
• Hands-on implementation of guardrails on Sagemaker.

Throughout our sessions, we aim to foster a collaborative and engaging environment, where participants can actively learn and discuss LLMs.

Get ready to embark on an enriching journey into the realm of LLMOps!

• Strong Python programming skills
• Familiarity with machine learning and deep learning concepts
• Basic understanding of Large Language Models (LLMs) and their applications
• Experience with cloud computing, particularly AWS services
• Basic knowledge of containerization and Kubernetes
• Familiarity with MLOps concepts and practices
• AWS Account with full access to Sagemaker, EKS, and Bedrock (as explicitly stated in the outline)