[NEW] The Ultimate Generative AI Leader Cert. Training

Master the fundamentals of machine learning, from raw data to trained models, covering data quality and formats, the end-to-end lifecycle, and the three paradigms: supervised, unsupervised, and reinforcement learning.

Explore the three-step machine learning process—training data, machine learning algorithm, and a predictive model—along with data types and supervised, unsupervised, and reinforcement learning that enable real-world inferencing.

Explore data types in machine learning, including labeled and unlabeled, structured and unstructured data, time series data, and how data preparation shapes supervised and unsupervised learning.

Learn how supervised learning uses labeled data to train models for classification and regression, with examples from email spam detection and classifying fruits by color and shape.

Explore regression in supervised learning, predicting continuous outputs from input features like house prices and car fuel efficiency, and compare regression with classification in labeled data.

Explore unsupervised learning with unlabeled data to discover hidden patterns through clustering, dimensionality reduction, anomaly detection, and density estimation, where algorithms learn without predefined outputs.

Explore unsupervised learning, including anomaly detection and density estimation, to spot unusual patterns, cluster similar data, and map data distributions for insights like fraud detection, customer concentration, and network monitoring.

Reinforcement learning trains an agent to maximize rewards by trial and error within an environment, illustrated by the snake game's state and actions.

Trace the machine learning lifecycle from data ingestion and preparation to model deployment and management. Leverage Google Cloud tools like Vertex AI for training, evaluation, deployment, monitoring, and governance.

Prioritize data quality and accessibility to boost AI performance, value, and timeliness, ensuring completeness, consistency, relevance, availability, usability, security, and governance.

Master the machine learning lifecycle from data ingestion to deployment, focusing on data quality, labeled vs unlabeled data, and supervised, unsupervised, and reinforcement learning with human feedback.

Explore Google's foundation models and their role in the generative AI landscape, including Gemini's multimodal capabilities, Gemma's efficient deployment, and text-to-image and video generation for creative and multimedia projects.

Explore the five-layer generative ai landscape—from infrastructure to applications—and learn how each layer impacts cost, scalability, and business value. Identify opportunities and challenges across infrastructure, models, platforms, agents, and applications.

Explore Google's Gemini family, a natively multimodal ai suite for text, images, audio, and video, including image generation and editing with Nano Banana and Nano Banana Pro.

Explore the Gemini family’s multimodal capabilities, from high-throughput Flashlight and thinking variants to edge-friendly Nano models, and learn practical use cases across enterprise apps, content generation, and real-time services.

Explore the Gemini app, test foundation models, and work with gems to tailor ai assistance. Learn model options, tools, and sources to verify accuracy and create multi-model outputs.

Gemma offers lightweight, open by design AI models with open weights, enabling customization on your own infrastructure, responsible AI, and specialized variants for coding, vision, and safety moderation.

Compare Gemma and Gemini in Google AI Studio, noting 1 billion, 4 billion, 12 billion tradeoffs: Gemini's power for multimodal tasks versus Gemma's accessible open source customization for local deployment.

Explore Imagine's diffusion-based, high-fidelity text-to-image generation from natural language prompts, including editing, style transfer, and variations, for marketing, product design, and education, scalable via Google Cloud Vertex AI.

Explore how Imagine in Google AI Studio uses prompts and aspect ratios to generate artistic or photorealistic images for leaders, training materials, social media, and product mockups.

Explore Google's Vue text-to-video model, enabling high-definition video from text prompts, with image or video inputs, audio generation, and tools for editing, styling, and rapid prototyping.

Demonstrate generating a cinematic video in Google AI Studio by configuring a close-up coffee mug shot, using model view two, 16 by 9, five seconds, and 24 fps.

Generate a video of a samurai saying best of luck on your generative AI leader exam using the pro model VO 3.1; rendering may take one to two minutes.

Explore Google's foundation models and Gemini's multimodal ecosystem, from core infrastructure and model categories to generation tools, governance, and real-world business implications for automation and user experience.

Explore how Google Cloud leads the generative ai space with an ai first vision and enterprise ready platform, offering secure, scalable, responsible ai via integrated tools and tpus.

Google's AI first vision rethinks all products with AI at the core, translating breakthroughs into offerings like Gemini, Imagine, and View, and building TPUs and AI infrastructure for cloud.

Explore Google Cloud's enterprise ready ai platform for generative ai, built on five pillars—responsible, secure, private, reliable, and scalable—to enable ethical, secure, private, reliable, and scalable ai deployments.

Discover how Google's comprehensive AI ecosystem integrates generative AI across workspace, analytics, and cloud services to accelerate innovation and enable seamless data synergy. Embrace the open approach and open-source flexibility.

Explore Google Cloud's AI-optimized infrastructure for large-scale generative AI, powered by NVIDIA GPUs and TPUs for fast training, efficient inference, and scalable foundation models.

Discover how Google Cloud secures data control with encryption, privacy, governance, and transparency for enterprise AI. Explore democratizing AI through pre-trained models, APIs, and low-code tools on Vertex AI.

Explore Google Cloud's strengths in generative AI, including TPU-based infrastructure, an open ecosystem, and a responsible, secure, privacy-conscious, scalable enterprise-ready platform that integrates AI across Google products.

Explore Google's ready-to-use generative AI solutions, including Gemini apps, Gemini Enterprise, workspace integration, Vertex AI search, and the customer engagement suite, while building customized AI assistants.

Discover how the Gemini app enables direct interaction with Google's Gemini models, featuring Gemini Flash, built-in fact checking, real-time camera conversations, and premium plans like Eye Pro and I ultra.

Create customized Gemini gems to tailor AI assistants for specific tasks by configuring name, instructions, and knowledge. Test and compare gem responses with regular Gemini to ensure brand voice consistency.

Explore instructions for Gemini, formerly saved info, and how persistent memory across all conversations enables user-controlled, global guidance. Access, edit, and test saved info to influence Gemini outputs.

Discover Gemini Enterprise, Google's full stack platform for unifying models, data, and tools to automate workflows with intelligent agents.

Explore how Gemini for Google Workspace brings AI-powered collaboration to Gmail, Docs, Sheets, Slides, Meet, and Drive, enhancing writing, summarizing, brainstorming, and data presentation.

Explore how Gemini in Google Slides accelerates productivity by generating images, creating new slides, summarizing content, and rewriting text, all from the Gemini panel and prompts.

Explore how Gemini in Google Sheets generates a student data table and creates a column chart of midterm versus final scores, accelerating data analysis and visualization.

Discover how Google Cloud's enterprise search and Agent Search use generative AI to ground foundation models, index your content, and deliver AI-powered relevance, summaries, and direct answers.

Generative AI powers Google's customer engagement suite for better service. It centers on conversational agents, Agent Assist, and customer experience insights within Google Cloud contact center as a service.

Explore Google's Notebook LM hands-on to upload PDFs, text, and web articles, and generate summaries, study guides, and audio podcast overviews with citations.

Explore prebuilt gen ai offerings for productivity, including the Gemini app with real-time camera and image generation, plus no-code agent design and enterprise integrations across Google Workspace.

Explore Google Cloud tools for building and deploying AI applications, including Vertex AI, Model Garden, RAG and grounding, AI search, AutoML on Vertex AI, and AI Studio prompts.

Discover Google Cloud's Gemini Enterprise Agent Platform, a unified AI platform for end-to-end development, governance, and optimization of agent-based models and workflows.

Explore Vertex AI and Model Garden to discover, test, customize, and deploy models using the dashboard, notebooks, studio, and model registry, featuring pre-trained and open models.

Explore grounding and rag (retrieval augmented generation) to connect large language models to verifiable sources, improve accuracy, and reduce hallucinations in enterprise applications.

Connect your private enterprise data to LLMs with Rack Engine on the Gemini Enterprise Agent platform, grounding answers and reducing hallucinations through developer-controlled, custom Rack pipelines.

Empower teams with AutoML on Vertex AI to build models without machine learning skills. Automates data prep, model selection, and tuning for image classification, text classification, object detection, tabular forecasting.

Explore Google AI Studio's three prompt types—free form prompts, structured output, and chat prompts—demonstrating when to use each for creative content, consistent data formats, and interactive experiences.

Discover Google Cloud's Gemini Enterprise Agent Platform, an end-to-end AI lifecycle suite with model access, agent development, deployment, governance, and optimization, plus rack-based grounding and AutoML for non-experts.

Explore how generative AI agents reason, plan, and interact with real-world environments to drive enterprise value. Learn when to build custom agents with Gemini Enterprise and cloud services extend capabilities.

Explore Vertex AI Agent Builder to create custom, enterprise-grade AI agents with low-code tooling, data connectivity, foundation model integration, tools, and testable deployment for customer service, internal support, and automation.

Explore how agent tooling extends language models by connecting to live data and actions with Google Cloud data storage, compute, and pre-built AI APIs like Speech-to-Text and Vision API.

Discover how gen ai agents interact with the external environment through functions and function calling. Leverage extensions, plugins, and data stores to access up-to-date knowledge bases and perform actions.

Learn to choose between Google AI Studio for rapid prototyping and learning with Google's latest models, and Vertex AI Studio for production-grade, enterprise AI with MLOps, security, and data integration.

Discover Vertex AI agent builder for creating custom generative AI agents in a low-code environment, with data connectivity, foundation model integration, tools for actions and external APIs, testing, and deployment.

Explore foundation model limitations and learn Google Cloud strategies—grounding, retrieval augmented generation, prompt engineering, fine tuning, and human in the loop—for dependable AI with continuous monitoring in Vertex AI.

Identify the common limitations of foundation models, including data dependency, knowledge cutoff, bias and fairness, hallucinations, and edge cases, and learn strategies to address them for robust, reliable AI solutions.

Discover Google Cloud recommended practices to address foundation model limitations, including grounding and retrieval augmented generation, prompt engineering, fine-tuning, and human in the loop, to improve accuracy, relevance, and safety.

Understand grounding in llms by linking outputs to first party, third party, and world data to boost accuracy for use cases like internal hr tools and market analysis.

Explore how RAC enhances LLM outputs by grounding responses in external data, improving accuracy and reducing hallucinations. Leverage up-to-date information, contextual relevance, transparent citations, and better handling of complex queries.

Explore Google Cloud grounding offerings and RAC with Vertex AI Search and pre-built rag, using APIs and Google Search for real-time, context-aware generation.

Learn why continuous monitoring and evaluation sustain AI performance, detect data drift, concept drift, and bias, and uphold security with Vertex AI tools such as model registry and feature store.

Overcoming foundation model limitations by grounding outputs with knowledge via retrieval-augmented methods, prompt engineering, and fine-tuning, reducing hallucinations using first party data, third party data, and information under human oversight.

Explore prompt engineering to maximize large language model performance, mastering zero-shot to few-shot prompting, role-based strategies, prompt chaining, and tuning with temperature, top p, and top k.

Master the art and science of prompt engineering to guide large language models toward accurate, relevant outputs. Learn structured design and creative refinement to unlock a model's full potential.

Master essential prompting techniques—zero-shot, one-shot, few-shot, and role prompting—to guide large language models, shaping context, instruction, tone, and the quality of responses.

Discover advanced prompting techniques for guiding large language models, including prompt chaining, chain-of-thought reasoning, and react prompting, to handle multi-step problems and tool-assisted tasks.

Explore how inference parameters such as temperature, top P, and top K shape model text generation, balancing creativity, reliability, and ensuring input and output lengths fit the context window.

Master prompt engineering to guide llms with zero-shot, one-shot, and few-shot prompts, role prompting, and advanced techniques like chain of thought and tool usage for accurate outputs.

Bridge AI potential and business reality by implementing transformational generative AI solutions across industries, outlining integration roadmaps, decision factors, and metrics for ROI and organizational impact.

Identify key generative AI solution types for business, including content creation and augmentation, media generation, code generation and assistance, data synthesis, personalized experiences, and automation with AI agents.

Identify key factors shaping Gen AI needs in your organization, from business requirements and technical constraints to scale, customization, and data privacy. Consider latency and deployment for fit solution.

Strategically select a Genai solution by aligning with business goals, evaluating technical feasibility, piloting options, and ensuring data strategy, privacy, security, governance, and ROI.

Identify seven steps to integrate Genii into an organization, including ownership and governance, data readiness, ai solution development, pilot testing, change management, scaled deployment, and continuous monitoring.

Define metrics and smart KPIs aligned to business goals. Track baselines, measure time saved and cost reductions; analyze quantitative and qualitative impacts, and calculate ROI to demonstrate GenAI value.

Drive transformational generative ai solutions across content creation, media and code generation, data synthesis, and personalized experiences, guided by six key elements and seven integration steps to measure ROI.