1. Knowledge Enhancement:

  - Participants will acquire a thorough understanding of the architecture and features that differentiate enterprise RAG from traditional RAG systems.

  - They will grasp the intricacies of memory storage in an enterprise context and its integration with semantic caching, enhancing their knowledge of system efficiency and scalability.

2. Practical Implementation Skills:

  - Participants will develop hands-on skills in building semantic caching from scratch, understanding the core concepts, and implementing them with real-world examples.

  - The practical sessions using Redis and GCP will equip students with the ability to set up and configure semantic caching, leveraging the benefits of fast performance and low latency.

3. Problem-Solving Proficiency:

  - Participants will be empowered to address complex challenges related to semantic caching by considering factors such as context, query policies, and distance metrics.

  - The course will provide insights into optimizing semantic caching systems, allowing students to develop problem-solving proficiency for various scenarios.

4. Integration of Cutting-Edge Technologies:

  - Participants will gain exposure to cutting-edge technologies, including Redis, Google Cloud Platform, and Vertex AI, understanding their roles in enhancing semantic caching capabilities.

  - The integration of Google's Vertex AI for coherent text responses will give students insights into the latest advancements in the field.

Participants will get access to our home-brewed Online LLM for free

Course Prerequisites:

Proficiency in Python and a solid understanding of RAGs, as well as Encoder and Decoder models.

If you feel the need for a more foundational course, consider checking out my other offering on LLMs: https://maven.com/boring-bot/ml-system-design

Tools utilized in this course include VS Code, UNIX terminal, Jupyter Notebooks, and Conda package management, ensuring a hands-on and practical learning experience.