Build and Deploy ML Solutions with Vertex AI: From Training to Production
Build Smarter, Deploy Faster: Power ML at Scale with Vertex AI.
- Master AutoML and custom models
- Automate ML workflows
- Scalable, reproducible experiments
- Online predictions and explanations
- Enhance analytics-driven ML solutions
- Align with industry
- 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
"Build and Deploy Machine Learning Solutions on Vertex AI" is an intermediate-level course designed for data scientists and ML engineers aiming to master end-to-end ML workflows on Google Cloud. Through four hands-on labs, learners will explore Vertex AI's AutoML, custom training, and pipeline tools to train, tune, explain, and deploy models. The course covers real-world use cases, such as image classification for damaged car parts and predicting customer churn using BigQuery ML, while emphasizing containerization, pipeline automation, and model deployment best practices. Completing the course earns an official skill badge, validating your expertise in production-grade ML solutions.
Google's Build and Deploy Machine Learning Solutions on Vertex AI course Outline
Learn the Build and Deploy Machine Learning Solutions on Vertex AI
Ready to build and deploy cutting-edge ML models? Enroll in Build and Deploy Machine Learning Solutions on Vertex AI on Google Cloud Skills Boost. This course combines hands-on labs with real-world scenarios—from training custom image classifiers to automating pipelines and deploying models for predictions. Whether you're refining your ML engineering skills or preparing for production challenges, you'll earn a skill badge to prove your expertise. Click the link to start transforming raw data into actionable, scalable AI solutions today!
Start your journey today:
Course Structure Includes:
Lab 1: Train a custom AutoML Vision model to identify damaged car parts (1.5 hours).
Lab 2: Deploy a BigQuery ML XGBoost classifier to Vertex AI for churn prediction, including batch/online predictions (1.5 hours).
Lab 3: Create ML pipelines with Vertex AI Pipelines (1.5 hours).
Lab 4: Challenge Lab—End-to-end model training, deployment, and pipeline creation (2 hours).
Key Tools: Vertex AI Notebooks, Artifact Registry, BigQuery ML, KFP SDK, Vertex Endpoints.
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
Master Vertex AI's AutoML, pipelines, and deployment tools. Train models, automate workflows, and deploy solutions with hands-on labs on image classification, churn prediction, and more.
Pre-requisites
Prior to enrolling in this skill badge course, it is recommended that you have prior experience with Python programming as covered in the Google Python Class and that you complete the Machine Learning Crash Course. See the Google Cloud Tech AI Simplified video series for an overview of Vertex AI.
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Generative AI in Real World Workplaces PDF Free Download | SPOTO
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Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
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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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