Dive into Learning Explore the rich courses offerings of MammothClub.

Ready to unleash the full potential of Google AI Studio and create jaw-dropping apps that amaze and inspire? This course puts cutting-edge AI tools at your fingertips.

What if you could turn your business ideas into fully functional AI-powered apps—without needing a technical background? This course makes it possible.

Test your knowledge (with 45+ questions and unlimited attempts!) on data preparation: identify needed source documents that provide necessary knowledge and quality for a RAG application, identify prompt/response pairs that align with a given model task, use tools and metrics to evaluate retrieval performance, design retrieval systems using advanced chunking strategies, explain the role of re-ranking in the information retrieval process.

Test your knowledge (with 45+ questions and unlimited attempts!) on Application Development: Create tools needed to extract data for a given data retrieval need, select Langchain and similar tools for use in a Generative AI application, identify how prompt formats can change model outputs and results, qualitatively assess responses to identify common issues such as quality and safety, select chunking strategy based on model and retrieval evaluation.

Test your knowledge (with 45+ questions and unlimited attempts!) on Assembling and Deploying Apps: Code a chain using a pyfunc model with pre- and post-processing, control access to resources from model serving endpoints, code a simple chain according to requirements, code a simple chain using LangChain, choose RAG app elements: flavor, embedding model, retriever, dependencies, examples, signature, register the model to Unity Catalog using MLflow.

Test your knowledge (with 45+ questions and unlimited attempts!) on Assembling and Deploying Apps: Sequence the steps needed to deploy an endpoint for a basic RAG application, create and query a Vector Search index, identify how to serve an LLM application that leverages Foundation Model APIs, identify resources needed to serve features for a RAG application, explain the key concepts and components of Mosaic AI Vector Search, identify batch inference workloads and apply ai_query() appropriately.

Crafting Customized Applications with OpenAI ChatGPT API Using Embeddings in Python