Google Generative AI Leader Certification Program

Discover how generative AI creates, synthesizes, discovers, and streamlines text, documents, visuals, audio, video, and code, demonstrated through three case studies.

Learn how generating text and documents drives faster, more consistent, compliant customer interactions with generative AI, including Gemini in Gmail, automated memos, and enterprise AI on Google Cloud.

Explore generating visuals, audio, and video with Google GenAI, including Imogen image generation, VO video/audio, Google Vids, and Gemini-powered slides, with practical demos and real-time prompts.

Generate code and data to accelerate legacy modernization and software development with Gemini Code Assist, producing unit tests, starter code, and synthetic data across Google Cloud and Colab Enterprise.

Synthesize capability turns complexity into clarity by summarizing long documents and datasets into clear, actionable reports for leaders using Notebook LM Enterprise and Gemini in Looker.

Capture meeting insights with Gemini in Google Meet by automatically generating structured notes, including discussion points, decisions, and actionable items, then email them and save to Google Drive for handoffs.

Learn to turn multisource data into actionable insights with Gemini in BigQuery, using natural language to ask questions and generate reports for non-technical users.

Discover hidden patterns in data, monitor real-time events, and surface early insights using generative AI, Vertex AI forecasting, search, NotebookLM, Gemini in Google Drive, and Google AgentSpace.

Automate workflows with generative AI to reduce manual work, including documentation, reviews, and format conversion, and enable contract review, alerts, and multilingual support via Document AI and text-to-speech, speech-to-text, translation.

Explore multimodal generative AI in banking, combining text, images, audio, video, and documents to speed onboarding, verify IDs, analyze notes, and improve risk and fraud detection.

Explore Vertex AI, Google's unified platform to build, train, and deploy AI models at scale across modalities, with Model Garden and the generative AI app builder for enterprise solutions.

Harness generative AI to generate new content, synthesize complex information, discover hidden patterns, and streamline workflows across banking operations.

Review core Gen AI concepts and the Google Gen AI leader exam format through a quiz, highlighting multimodal capabilities and Gemini’s high-value applications.

Explore foundation models—large-scale, broadly trained, flexible and adaptable generative ai that power diverse tasks—from contract understanding to customer service—plus fine-tuning with proprietary banking data and Gemini, Imogen, Chirp.

Review foundation models and large language models through quiz questions, clarifying how LLMs fit as a subset of foundation models and how supervised learning enables text-based summaries across business functions.

Explore how foundation models power generative AI by prompting inputs in natural language to unlock knowledge, generate content, or summarize across text, images, and audio.

Explore foundation models, prompts, and multimodal capabilities in banking, from grounding and RAG to role prompting, prompt design, and responsible AI across data, models, and tools.

Discover how Google's AI-first strategy fuels enterprise productivity with Gemini and Gemma across Vertex AI, Google Cloud, and Workspace, while ensuring security, compliance, and responsible AI.

Google Cloud's generative AI platform abstracts infrastructure, letting leaders focus on core problems while Vertex AI handles upgrades, security, and open standards with TensorFlow and PyTorch.

Align leadership and frontline teams to build a Gen AI strategy around six pillars—strategic focus, exploration, responsible AI, resourcing, impact, and continuous improvement.

Identify who drives Gen AI strategy and funding, from executives to mid-level managers. Explain how top-down and bottom-up inputs shape use cases, ethics, and the SAFE framework.

Keep humans at the forefront of gen ai by pairing human judgment with ai efficiency, guiding strategy, creativity, and decision-making while ai handles automation and analysis under continuous human-in-the-loop.

Explore how generative AI enhances banking work through augmentation versus automation, covering compliance, data extraction, portfolio analysis, governance, and Google Cloud GenAI solutions.

Align leaders on core ai and data fundamentals, data quality, and the lifecycle from ingest to manage, plus techniques like grounding, RAG, prompt engineering, and fine-tuning of foundation models.

Distinguish artificial intelligence, machine learning, and generative artificial intelligence, and learn how data types, data requirements, and learning approaches enable generative artificial intelligence applications across business contexts.

Learn how data quality, accessibility, and data types shape AI, ML, and GenAI outcomes, driving informed decisions, efficiency, and predictive power in modern banking.

Assess your understanding of AI, ML, and Gen AI through a section review and quiz that covers data quality, representativeness, data accessibility, and multimodal data for generating new content.

Explore how data quality and accessibility shape machine learning. Compare supervised, unsupervised, and reinforcement learning with Google Cloud banking examples.

Explore machine learning approaches for credit risk, fraud detection, and trading through questions on supervised, unsupervised, and reinforcement learning, plus labeled versus unlabeled data.

Deploy and monitor enterprise-grade AI by mastering data ingestion, preparation, training, deployment, and ongoing model management with Vertex AI, BigQuery, and Google Cloud data tools.

Explore the machine learning life cycle from data ingestion and preparation to model training, deployment, management, reinforcement learning basics, and handling unstructured data.

Discover how foundation models power modern generative ai, comparing large language and diffusion models, and apply grounding, retrieval-augmented generation, and governance to manage bias, hallucinations, and knowledge cutoffs.

Review key concepts in deep learning, generative AI, and foundation models. Explore large language models and diffusion models that create original content across applications.

Select the right model by evaluating modality (text, image, audio, or multimodal), context window, security, availability, cost, performance, fine-tuning, and ease of integration for banking use cases.

Explore Google's Vertex AI platform to discover, deploy, and customize multimodal machine learning models—Gemini, Gemma, Imogen, and Veo—on a single unified platform powering scalable business solutions.

Explore modality and context window considerations in generative ai, assess availability and reliability, and enable integration through APIs and SDKs for text, image, audio, and video tasks.

Enhance foundation model performance by grounding responses to trusted data, using retrieval augmented generation (RAG) and fine-tuning, and balancing with human oversight for reliable enterprise AI.

Explore grounding to link outputs to real-world data, fine-tuning for industry-specific knowledge, and the roles of large-language models, diffusion models, and prompt engineering in reliable, enterprise-ready generative AI.

Develop secure, responsible AI by protecting data, controlling model access, and monitoring production to meet regulatory expectations. Learn to balance innovation with trust and ethical use across the AI lifecycle.

build secure, responsible ai to prevent harm and deliver ethical, fair, and trusted outcomes in banking. ensure transparency, privacy, and accountability across data, models, and systems throughout the ai lifecycle.

Review data quality, bias, and responsible AI practices highlighted in the section review and quiz. Apply ethical, transparent, and secure AI principles from the Safe framework and explainable AI concepts.

Explore the GenAI architecture and solution stack across five layers: infrastructure, models, platform, agents, and applications, highlighting enterprise use, AI at the edge with LightRT, and governance realities.

Explore the GenAI landscape by examining infrastructure, models, platform, agents, and GenAI powered applications to guide secure, scalable, and regulatory-ready AI initiatives in banking.

Explore how Gen AI agents power real-world applications by coordinating multiple specialized agents to analyze data, respond to natural language, automate multi-step tasks, and personalize user experiences in banking scenarios.

Explore how conversational and workflow AI agents understand intent, leverage tools, and automate banking tasks. Learn how AI agents unite reasoning loops with tools to boost customer experience and efficiency.

Review the generative AI landscape's layers—agents, models, infrastructure, and applications—and learn how reasoning loops and external tools empower agents to analyze data, make decisions, and take actions.

Understand how the Gen AI platform maps to infrastructure with Vertex AI, offering open frameworks, integrated tooling, and MLOps components like feature store, model registry, and pipelines for enterprise-scale AI.

Master the Google generative AI landscape by reviewing the platform layer that unifies infrastructure, tools, and workflows, Vertex AI's end-to-end ML lifecycle capabilities, and the feature store plus model registry.

Explore the model layer powered by Vertex AI, using Model Garden's Gemini, Imogen, Veo, Chirp, Gemma, and more, and learn the end-to-end model lifecycle from data to deployment.

Explore Vertex AI options from Model Garden's Gemini Pro to AutoML and fully custom workflows, and learn how to evaluate models before deployment.

Explore the infrastructure layer as the foundation of AI systems. Learn about high-performance computing with GPUs and TPUs, AI hypercomputers, fast networking, and scalable storage for generative AI workloads.

Discover the infrastructure layer powering training and inference with CPUs, GPUs, TPUs, and cloud storage, and how Vertex AI enables open, flexible workflows with TensorFlow or PyTorch.

Explore AI on the edge, running models near data sources for responsiveness and privacy, using light runtime and Gemini Nano, with Vertex AI coordinating conversion, packaging into containers, and monitoring.

Discover how edge computing enables real-time AI decisions by processing data near the source, and explore Light Runtime and Gemini Nano for on-device inference, including Pixel Recorder.

Balance people, cost, and time to decide between pre-built Gen AI solutions and custom builds, considering scale, customization, and interaction for sustainable business value.

Identify developers' roles in generative AI projects, focusing on building and deploying AI agents and integrating AI capabilities into applications. Examine how complexity and usage-based pricing influence Gen-AI development.

Design scalable genai solutions by balancing scale, customization, and governance for both small teams and enterprise. Focus on user experience, latency, accuracy, explainability, and ongoing regulatory compliance.

Explore how LightRT optimizes AI performance on edge and mobile devices, enabling on-device AI, while considering pre-built Gen AI apps and Gemini Nano in Pixel Recorder.

Move from Gen AI architecture to delivering real value by guiding leadership and change management as AI agents transform operations, scale responsibly, and enable measurable impact across individuals and organizations.

Gemini for Google Workspace embeds generative AI into Google Docs, Sheets, Slides, Meet, and Drive, with a side panel that summarizes, analyzes, and creates content from emails and files.

Master prompting techniques to shape AI outputs, using zero-shot, one-shot, and few-shot methods with clear instructions and examples to enhance precision and business relevance.

Learn to reuse prompts with Gemini and Gems by applying prompt chaining, saved instructions, and task-specific gems, enhanced by retrieval-augmented generation for grounded, context-aware outputs.

Discover Notebook LM, an ai-first notebook that grounds responses in your own documents for accurate, traceable insights, with Notebook LM Enterprise features for governance and secure collaboration.

Explore modern generative AI agents, their three components—foundation models, tools, and the reasoning loop—and how retrieval-augmented generation grounds responses for responsible scale.

Learn to build reliable AI agents by configuring models for grounding and safety, adjusting token counts, temperature, Top-P, and output length, with Google AI Studio and Vertex AI Studio.

Review key generative AI concepts, including token count, temperature, and top-p sampling, and explain how APIs connect prompts to Gemini models in Google AI Studio and Vertex AI Studio.

Plan ahead for integration, impact, and change by establishing a clear vision aligned with the bank’s strategy, prioritizing high-impact use cases, and investing in AI capability and talent.

Capstone session unites strategy, architecture, governance, and cross-industry applications as you design and demonstrate a generative AI solution on Google Cloud, showcasing leadership-level thinking and Gen AI Leader Certificate readiness.

Capstone: deploy generative AI and AI agents across an organization with a roadmap. Translate high-impact ideas into executable implementation plans across business functions, using hands-on design and case studies.

Discover how a unified knowledge base and 24-7 ai assistant revolutionize training and policy access. See how automating repetitive tasks and data workflows cuts effort, errors, and response times.

Create a Google account and register for Google Cloud to activate $300 in free credits, verify identity, set up billing, and access the Cloud console for service exploration.

Explore Vertex AI, Google Cloud's end-to-end AI platform for discovering, customizing, and deploying models in one workspace with Model Garden, Agent Builder, Vector Search, and AI applications.

Create a multi-region cloud bucket with standard storage; organize folders for training materials and internal documents, then upload pdfs, word, and text files to power Vertex AI search and RAG.

Build a RAG engine in Vertex AI by configuring corpora, embeddings, and a managed vector store, then enable grounding to retrieve internal documents and generate grounded responses.

Design and connect the conversational AI flow for a banking information dialogue, defining intents, greetings, and routing to a grounded playbook with data store integration for a production-ready chatbot.