Whether you are starting your AI engineering journey or expanding your existing skills, MammothClub offers a practical catalog of courses, paths, and focused learning opportunities.
What if your 3D web projects were easy to maintain—and ready to grow? This bundle shows you how.
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.
Test your knowledge (with 45+ questions and unlimited attempts!) on governance: Use masking techniques as guardrails to meet a performance objective, select guardrail techniques to protect against malicious user inputs to a Gen AI application, recommend an alternative for problematic text mitigation in a data source feeding a RAG application, use legal and licensing requirements for data sources to avoid legal risk, recommend alternatives for problematic text mitigation in a data source feeding a GenAI application.
Fungible token development with TypeScript smart contracts!
