GAIL study notes
- Fundamentals of generative AI Generative AI Leader notes on core AI terms, machine learning approaches, the ML lifecycle, choosing a foundation model, use cases, data types and quality, the layers of the gen AI landscape, and Google's models Gemini, Gemma, Imagen and Veo. 30%
- Google Cloud's generative AI offerings Generative AI Leader notes on Google Cloud's gen AI strengths, AI infrastructure, the Gemini app, Gemini Enterprise, Gemini for Workspace, Customer Engagement Suite, Agent Search, Agent Platform, Model Garden, RAG offerings, agent tooling, pre-built AI APIs, Agent Studio and Google AI Studio. 35%
- Techniques to improve model output Generative AI Leader notes on foundation model limitations, hallucinations, grounding, RAG, fine-tuning, human in the loop, monitoring and evaluation, prompt engineering techniques, and sampling parameters such as temperature and top-p. 20%
- Business strategies for generative AI Generative AI Leader notes on choosing and implementing gen AI solutions, measuring impact and ROI, change management, secure AI and Google's Secure AI Framework, Google Cloud security tools, and responsible AI including privacy, bias, fairness, accountability and explainability. 15%