Introduction to AI and Machine Learning on Google Cloud
Master AI & ML on Google Cloud: From Foundations to Generative AI
- Holistic AI Understanding
- Master the data-to-AI workflow, from infrastructure to deployment
- Master the Generative AI Expertise
- Align with industry
- Gain cloud-based AI/ML implementation skills
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
This course, available via Google Cloud Skills Boost, provides a comprehensive introduction to Google Cloud's AI and machine learning (ML) technologies. Designed for data scientists, AI developers, and ML engineers, it explores the end-to-end data-to-AI lifecycle, covering foundational concepts, development tools, and generative AI solutions. Through hands-on labs and real-world examples, learners gain practical experience with Google Cloud's infrastructure (e.g., Vertex AI, BigQuery ML), pre-trained APIs, AutoML, and generative AI toolkits like Gemini. The course emphasizes building predictive and generative AI projects while navigating Google Cloud's ecosystem.
Google's Beginner-friendly Introduction to AI and Machine Learning on Google Cloud course Outline
Learn the Introduction to AI and Machine Learning on Google Cloud
Ready to dive into AI and machine learning on Google Cloud? Whether you're building predictive models or exploring generative AI, the Introduction to AI and Machine Learning on Google Cloud course equips you with the tools and knowledge to succeed. Learn through labs, videos, and quizzes how to leverage Vertex AI, AutoML, and Gemini for real-world projects. Start transforming data into AI solutions today!
Start your journey today:
Course Structure Includes:
Introduction:
- Overview of Google Cloud's AI framework (foundations, development, solutions).
AI Foundations:
- Cloud infrastructure (compute, storage).
- Data-to-AI tools (BigQuery ML for model building).
AI Development Options:
- Pre-trained APIs vs. AutoML vs. custom code (pros/cons).
AI Development Workflow:
- End-to-end ML workflow (data prep, model training, deployment via Vertex AI).
- Automation with Vertex AI Pipelines.
Generative AI:
- Gen AI workflows, Gemini multimodal models, Gen AI Studio, Model Garden, and prompt design.
Summary:
- Recap of key tools, technologies, and workflows.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn Google Cloud's AI/ML tools for predictive and generative projects. Build models with Vertex AI, BigQuery ML, and Gemini through labs and quizzes.
Pre-requisites
No Experience needed.
Generative AI in Real World Workplaces PDF Free Download | SPOTO
Generative AI in Real World Workplaces PDF Free Download | SPOTO
Cours name: AI File Type: PDF
Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
Cours name: AI File Type: PDF
SPOTO Empowers You to Earn Your Certification.
Benefits of Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
Advantages of Using SPOTO's Exam Preparation Materials
SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. Their materials likely include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on Google Cloud tools and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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