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.
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Test your knowledge of core generative AI (gen AI) concepts and use cases! Including core gen AI fundamentals such as artificial intelligence, machine learning, natural language processing, large language and foundation models, multimodal and diffusion models, and prompt engineering; key machine learning approaches like supervised, unsupervised, and reinforcement learning; the machine learning lifecycle and supporting Google Cloud tools; selecting the right foundation model for business needs; real-world gen AI use cases for creating, summarizing, discovering, and automating content; and the role of data types, data quality, accessibility, and the differences between structured vs. unstructured and labeled vs. unlabeled data.
Test your knowledge of Google Cloud’s prebuilt generative AI offerings and AI-powered work! Including the functionality, use cases, and business value of the Gemini app and Gemini Advanced (such as Gems), Gemini Enterprise capabilities like Cloud NotebookLM API, multimodal search, and custom agents, and Gemini for Google Workspace for boosting productivity, collaboration, and decision-making across everyday business workflows.
Test your knowledge of how Google Cloud’s generative AI enhances customer experience! Including the functionality, use cases, and business benefits of external search solutions like Vertex AI Search and Google Search, and the value of Google’s Customer Engagement Suite, such as Conversational Agents, Agent Assist, Conversational Insights, and Google Cloud Contact Center as a Service for delivering smarter, faster, and more personalized customer interactions.
Test your knowledge of how Google Cloud empowers developers to build with generative AI and agents! Including the functionality, use cases, and business value of the Vertex AI Platform (such as Model Garden, Vertex AI Search, and AutoML), Google Cloud’s RAG offerings and APIs, and Vertex AI Agent Builder for creating custom agents, as well as the purpose and types of agent tooling, how agents use tools to interact with external systems, relevant Google Cloud services and prebuilt AI APIs, and when to use Vertex AI Studio versus Google AI Studio.
Test your knowledge of prompt engineering and grounding techniques for generative AI! Including the definition and importance of prompt engineering, common and advanced prompting techniques such as zero-shot, few-shot, role prompting, chain-of-thought, and ReAct, and when to use them; grounding concepts using first-party, third-party, and world data; the impact of retrieval-augmented generation (RAG) on model outputs; Google Cloud grounding options like Vertex AI Search, RAG APIs, and Google Search grounding; and how sampling parameters such as temperature, token limits, top-p, safety settings, and output length control model behavior.
