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Kursbewertung: [NEW] The Ultimate Generative AI Leader Cert. Training von Vladimir Raykov

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[Latest Syllabus] Pass The Generative AI Leader Exam On Your First Attempt | 2 Full Practice Exams & 160+ Quiz Questions

Lernformat Online Course

Unterkategorie Generative AI

Kurspreis $19.99 (list $10.99)

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Über Vladimir Raykov

[Latest Syllabus] Pass The Generative AI Leader Exam On Your First Attempt | 2 Full Practice Exams & 160+ Quiz Questions

Wer ist Vladimir Raykov?

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15,995 students

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Kurssprache

English

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Was lernst du im Kurs von Vladimir Raykov?

Comprehensive Preparation For The Google Cloud Generative AI Leader Exam: 8h High-Quality Video Content + A Total Of 263 Questions & Explanations.

[Up-To-Date - 2025 Exam Syllabus] Master The Generative AI Leader Exam - No Previous Knowledge Needed.

[Downloadable] Recap Of Key Concepts - PDF file (75 Pages).

Differentiate between Artificial Intelligence, Machine Learning, and Deep Learning.

Identify different data types used in Machine Learning and evaluate data quality requirements for successful projects

Explore the applications of Computer Vision and Natural Language Processing (NLP).

Learn the key steps involved in the Machine Learning process.

Distinguish and apply the main types of Machine Learning: Supervised, Unsupervised, Reinforcement, and Semi-Supervised Learning.

Map out the entire Machine Learning lifecycle including development, deployment, and maintenance phases

Assess data accessibility and quality issues that can impact Machine Learning project success

Explain how machine learning algorithms transform raw data into intelligent predictions and decisions

Map the current generative AI landscape and position Google's foundation models within the competitive ecosystem

Evaluate Gemini's multimodal capabilities for text, code, and reasoning tasks across different business applications

Compare Gemma's lightweight architecture with larger models and determine when efficiency trumps raw power

Analyze Imagen's text-to-image generation capabilities and assess its potential for creative and commercial projects

Select the most appropriate Google foundation model based on specific project requirements and constraints

Analyze Google's AI-first strategy and explain how it creates competitive advantages in the cloud computing market

Evaluate Google Cloud's enterprise-ready AI features including security, privacy, reliability, and scalability measures

Examine Google Cloud's Hypercomputer architecture, TPUs, and GPUs to understand their role in powering generative AI workloads

Determine the key factors that make Google Cloud suitable for scaling enterprise AI initiatives

Navigate Gemini App subscription tiers and select the right plan for personal or business needs

Understand Vertex AI Search and Google Search solutions in business applications

Discover Google Agentspace capabilities and recognize its applications across different industries

Explore how Gemini AI enhances Gmail, Docs, and Sheets for improved productivity

Understand conversational agents and customer service tools that improve engagement

Identify which prebuilt Google AI solutions best fit specific workflow challenges

Learn about RAG and grounding techniques that improve AI response accuracy and contextual relevance

Understand Vertex AI Platform's unified approach to the complete AI development lifecycle from training to deployment

Understand Vertex AI Agent Builder's capabilities for creating autonomous AI agents that handle multi-step tasks

Discover how Google Cloud services and APIs provide foundational tools for building sophisticated agent systems

Learn how AI agents interact with external environments through extensions, functions, and data stores to perform real-world actions

Understand Google Cloud's solutions like grounding, RAG, and prompt engineering for building more reliable AI systems

Identify common foundation model limitations including hallucinations, bias, and knowledge cutoffs that impact AI performance

Learn how continuous monitoring and evaluation using Vertex AI ensures robust, production-ready AI applications

Understand the fundamental principles of prompt engineering that combine creativity with systematic approaches for optimal LLM performance

Learn essential prompting techniques including zero-shot, few-shot, and role-based prompting for different use cases

Discover advanced strategies like chain-of-thought reasoning and inference parameters that control AI model behavior and output quality

Identify different types of generative AI business solutions and understand how they address real-world organizational challenges

Learn the essential steps and considerations for systematically integrating generative AI into organizational workflows

Understand key decision factors including business requirements, technical constraints, and ROI measurement for successful AI implementation

Understand why security must be integrated throughout every stage of the machine learning lifecycle from development to deployment

Learn Google's Secure AI Framework (SAIF) and how it addresses unique security challenges in generative AI systems

Discover Google Cloud security tools including IAM, Security Command Center, and monitoring services for comprehensive AI protection

Understand why responsible AI practices including transparency and ethics are essential for sustainable business success and stakeholder trust

Learn about privacy considerations in generative AI and discover protective measures like data anonymization and pseudonymization techniques

Discover how data quality impacts bias and fairness, and understand strategies for building accountable and explainable AI systems

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