Google Cloud Generative AI Leader Full Course 2026

Understand what Generative AI is, how it differs from traditional AI, how LLMs work, and why Gen AI is transforming business, innovation, and decision-making.

Learn essential Gen AI concepts including tokens, embeddings, attention, context windows, temperature, and prompting to understand how large language models generate results.

Explore foundation models, pre-training, model families like Gemini, open vs proprietary models, cost trade-offs, and how leaders select the right model for business needs.

Understand model parameters, hyperparameters, learning rate, batch size, overfitting, and how hyperparameter tuning impacts performance, cost, and scalability.

Learn prompt engineering fundamentals including zero-shot, few-shot, personas, formatting, iteration, and when prompting is better than fine-tuning models.

Understand encoder, decoder, and encoder-decoder architectures, transformers, multimodal models, and how architecture choices impact Gen AI business use cases.

Explore how Gen AI generates text and code for summarization, translation, development, customer support, and scalable content creation with quality governance.

Learn how Gen AI creates images and video, key use cases in marketing and design, personalization, virtual prototyping, and managing copyright risks.

Discover how Gen AI works with structured data, including text-to-SQL, data summarization, synthetic data generation, analytics, and secure data access.

Understand the complete Gen AI lifecycle from planning to monitoring, including deployment, governance, iteration, and the leader’s role in long-term success.

Learn why data quality, governance, security, provenance, and fine-tuning data are critical to building accurate, compliant, and valuable Gen AI solutions.

Explore why Gen AI runs in the cloud, the role of Google Cloud and Vertex AI, scalability, security, cost management, and strategic cloud decisions.

In this lecture, you’ll explore the core techniques that shape how generative AI systems produce results. We break down prompt engineering strategies, explain how diffusion models generate images and media, and introduce multimodal AI systems that work across text, images, audio, and more. This session strengthens your understanding of how modern foundation models are guided, generated, and combined in real-world enterprise use cases.

This lecture revisits the three fundamental machine learning paradigms through a leadership lens. You’ll learn how supervised, unsupervised, and reinforcement learning differ, where each is used in generative AI systems, and why techniques like reinforcement learning with human feedback matter for alignment and trust. The focus is on understanding learning behavior, not building models.

In this lecture, you’ll walk through the complete machine learning lifecycle — from data ingestion and preparation to training, deployment, and ongoing monitoring. You’ll see why AI systems require continuous oversight and how lifecycle thinking supports governance, reliability, and long-term business value. This perspective is essential for leaders responsible for AI outcomes, not just implementation.

This session explains the difference between labeled and unlabeled data and why that distinction matters for generative AI strategy. Using real business examples, you’ll understand the trade-offs between cost, speed, accuracy, and scalability when working with annotated data versus raw enterprise information. The lecture helps you evaluate data readiness for AI initiatives.

Learn how Vertex AI serves as Google Cloud’s unified platform for building, deploying, monitoring, and governing Generative AI across the full MLOps lifecycle.

Discover how to find, test, compare, and deploy Gemini and open-source models using the Vertex AI Model Garden with governance and cost control.

Understand how data scientists use Vertex AI Workbench for fast experimentation, collaboration, secure development, and efficient resource management.

Learn how Vertex AI Endpoints securely deploy models with auto-scaling, versioning, traffic splitting, and high availability for production workloads.

Explore how Vertex AI Pipelines automate training, evaluation, and deployment to support scalable, repeatable, and auditable MLOps workflows.

Learn how Google Cloud enables Responsible AI using safety filters, bias detection, explainability, human-in-the-loop, and governance best practices.

Understand how BigQuery and Cloud Storage support structured and unstructured data for Gen AI, fine-tuning, RAG, governance, and compliance.

Learn how applications interact with Gemini models using APIs, multimodal input, streaming responses, security controls, and cost monitoring.

Discover when and how to fine-tune Gemini models on Vertex AI using high-quality data to achieve domain-specific accuracy and differentiation.

Learn how prompt tuning efficiently customizes models using soft prompts for faster, cheaper specialization without full fine-tuning.

Understand LoRA as a cost-effective fine-tuning method that delivers high accuracy with reduced compute and efficient model specialization.

Learn how to build grounded enterprise search and conversational AI using RAG, Vertex AI Search, and Conversation for trusted responses.

Explore the no-code Generative AI Studio to design prompts, compare models, launch tuning jobs, and move ideas quickly into production.

Master advanced prompting techniques including chain-of-thought, structured outputs, system instructions, and prompt versioning.

Learn best practices for deploying models with auto-scaling, canary releases, monitoring, and secure access using Vertex AI Endpoints.

Understand how pipelines enable CI/CD for Gen AI, ensuring reproducibility, automation, governance, and faster innovation.

Learn how Vertex AI Metadata tracks lineage, versions, artifacts, and audit trails to support compliance and accountability.

Explore how to detect drift, monitor quality, set alerts, and maintain long-term ROI using Vertex AI Monitoring tools.

Learn how IAM enforces least privilege access, secures models and data, supports compliance, and protects Gen AI infrastructure.

Understand Gen AI cost drivers and optimization strategies including model selection, auto-scaling, prompt efficiency, and billing controls.

Learn how Gen AI integrates with Gmail, Docs, Sheets, and Meet to boost employee productivity and automate everyday workflows.

Explore how Vertex AI Extensions, APIs, and middleware enable secure integration with CRM, ERP, and external business systems.

Learn how Vertex AI Vector Search powers semantic search and RAG for accurate, grounded, enterprise Gen AI applications.

Understand best practices for collecting, cleaning, labeling, and formatting high-quality data for successful model fine-tuning.

Learn how to evaluate Gen AI models using automated metrics, human review, safety testing, and deployment readiness checks.

Explore upcoming trends in Vertex AI, multimodality, AI agents, governance, and how leaders future-proof Gen AI strategies.

This lecture presents a clear, layered view of the generative AI ecosystem — from underlying infrastructure to foundation models, platforms, agents, and end-user applications. You’ll learn how each layer builds on the previous one and where leaders must decide what to build, buy, or integrate. The goal is architectural clarity, not technical depth.

In this lecture, you’ll compare Google’s core foundation models and understand how each aligns to different enterprise needs. We cover Gemini for multimodal reasoning, Gemma for controlled deployments, Imagen for image generation, and Veo for video creation. You’ll focus on selecting the right model for the right business scenario, not on model internals.

This lecture introduces Retrieval-Augmented Generation, or RAG, as a core enterprise architecture for trustworthy AI. You’ll learn how RAG combines foundation models with private data sources to reduce hallucinations and improve accuracy. The session explains when to use prebuilt RAG solutions versus custom implementations, from a decision-maker’s perspective.

In this session, you’ll explore how AI agents move beyond simple chatbots into structured, action-oriented workflows. We focus on low-code agent creation, tool integration, memory, and governance considerations. This lecture is designed to help leaders understand how agents scale AI capabilities across business functions without deep engineering effort.

Learn the prompt engineering hierarchy from zero-shot prompting to fine-tuning and how leaders choose the most cost-effective method to improve model output.

Explore advanced prompting techniques including Chain-of-Thought reasoning and Retrieval-Augmented Generation for accurate, grounded enterprise AI.

Understand when to use prompting, RAG, or fine-tuning based on cost, speed, accuracy, and business requirements.

Learn how to collect, clean, format, and label high-quality data to successfully fine-tune Generative AI models.

Explore the fine-tuning workflow on Vertex AI, including model selection, hyperparameters, evaluation, and cost control.

Learn how LoRA and prompt tuning provide efficient, low-cost model customization without full fine-tuning.

Deep dive into RAG architecture, vector search, grounding, and how RAG prevents hallucinations in enterprise Gen AI.

Discover how data augmentation and synthetic data improve model robustness, privacy, and performance.

Learn how to detect and mitigate AI bias while promoting fairness and ethical use in enterprise generative AI deployments.

Understand prompt security risks, prompt injection defenses, safety filters, and governance best practices.

Learn how to prompt Gen AI for secure code generation, debugging, review, and developer productivity.

Master techniques for generating reliable structured outputs using JSON schemas, function calling, and validation.

Learn how to manage context windows, conversation memory, RAG integration, and cost-efficient context strategies.

Understand continuous evaluation, model drift, data drift, and how Vertex AI Monitoring maintains long-term performance.

Explore HITL workflows for high-risk AI use cases, quality control, accountability, and continuous improvement.

Learn how red teaming and adversarial testing identify vulnerabilities and strengthen Gen AI safety.

Understand bias sources, detection, mitigation strategies, and how to ensure fair, ethical AI systems.

Review key techniques for optimizing Gen AI output and prepare to shift from technical execution to business strategy.

Learn how to identify high-impact Gen AI use cases using value vs feasibility analysis to drive measurable business outcomes.

Understand how to calculate Gen AI ROI by analyzing costs, benefits, pilots, and post-deployment financial performance.

Learn change management strategies to drive Gen AI adoption, address resistance, and upskill teams across the organization.

Understand ethical, legal, privacy, and compliance considerations for deploying Generative AI in enterprise environments.

Learn how to build an AI governance framework covering ethics, performance, data governance, MLOps, and cost control.

Explore how to build a Gen AI Center of Excellence to scale expertise, standardize practices, and accelerate adoption.

Learn strategies to hire, reskill, retain, and organize Gen AI talent for long-term competitive advantage.

Learn how to measure Gen AI success using KPIs, dashboards, ROI metrics, and executive-ready impact storytelling.

Understand the responsibilities of a Gen AI Leader in strategy, governance, stakeholder management, and transformation.

Explore emerging Gen AI trends including multimodality, AI agents, efficient models, and staying current on Google Cloud.

This lecture focuses on the governance controls that make generative AI viable in enterprise and regulated environments. You’ll learn how privacy isolation, access controls, monitoring, and auditability apply to AI systems, and why leaders must actively shape these policies. The emphasis is on responsibility, trust, and compliance — not configuration.

In this final lecture, you’ll examine why data quality and accessibility directly determine the success or failure of generative AI systems. We connect poor data practices to hallucinations, bias, and operational risk, and explain what leaders must do to ensure AI systems remain reliable and defensible over time. This session reinforces the foundation of responsible AI leadership.

Gain last-minute exam tips, build confidence, and align your certification achievement with real-world AI leadership impact.

Get exam-focused strategies, domain review tips, scenario-based thinking, and time management guidance.

Avoid common exam pitfalls and learn time management strategies to maximize your score and performance.

Review key course takeaways, next steps for certification, hands-on practice, and your path as a Gen AI Leader