Vertex AI: 1 Day Foundations Course in New York, NY
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About the course:
Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom / In-Person Workshop
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate Provided
Refreshments: Lunch, tea/coffee, and snacks included
Course Overview
This Vertex AI course offers a deep dive into Google's unified platform for building and managing machine learning and generative AI models. You’ll explore the end-to-end ML lifecycle—from data preparation to model deployment—using AutoML and custom training options. Through hands-on projects, real-world examples, and expert guidance, you’ll learn to accelerate AI development with scalable cloud solutions.
Learning Objectives
By the end of this course, you will:
• Understand how to build and manage ML and generative AI models using Vertex AI
• Prepare and analyze datasets for training AI models
• Choose between AutoML and custom training options confidently
• Deploy production-ready ML systems with MLOps tools
• Leverage generative AI to solve business-focused challenges
Target Audience
• Machine learning practitioners
• Data scientists
• ML engineers
• Developers
• Business teams
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Agenda
Module 1: Introduction to Vertex AI
• What is Vertex AI and why it matters
• Key components and capabilities
• Vertex AI vs other ML platforms
• Walkthrough: Vertex AI dashboard
Module 2: Dataset Management & Preparation
• Importing and labeling datasets
• Preparing structured and unstructured data
• Managing data in Google Cloud Storage
• Exercise
Module 3: AutoML Basics
• When to use AutoML vs custom models
• Creating and training AutoML models
• Use cases: image, text, tabular data
• Activity
Module 4: Custom Model Training
• Notebook environments and custom code
• Training jobs and resource configuration
• Model registry and versioning
• Training
Module 5: Model Evaluation & Explainability
• Performance metrics and evaluation tools
• Model explainability features in Vertex AI
• Debugging ML pipelines
• Case study
Module 6: MLOps Practices with Vertex AI
• Introduction to MLOps on GCP
• Pipelines, CI/CD, and workflow automation
• Artifact management and monitoring
• Activity
Module 7: Deploying ML Models at Scale
• Deployment options: online & batch predictions
• Autoscaling and A/B testing setups
• Monitoring model drift and performance
• Exercise
Module 8: Introduction to Generative AI on Vertex AI
• Using pre-built generative AI models
• Fine-tuning with your own data
• Use cases: chatbots, content generation, search
• Hands-on activity
Module 9: Industry Use Cases & Best Practices
• AI across industries: retail, healthcare, finance
• Governance and ethical AI considerations
• Cost optimization strategies on GCP
• Group discussion
FAQs:
1. Do I need prior experience with Google Cloud?
Basic understanding of cloud and ML concepts is helpful, but beginners are welcome.
2. Will this course include hands-on lab work?
Yes, practical exercises with Vertex AI will be guided by an instructor.
3. Does this cover deep learning models?
Yes, custom training and generative AI workflows are included.
4. Is GPU usage covered in the session?
Yes, resource provisioning (including GPUs) is discussed during custom training.
5. Can I use this course to build real AI applications?
Yes, the skills gained will help you prototype and deploy real-world AI systems.
6. Will I learn MLOps and pipelines?
Yes, MLOps best practices and pipeline-building are included in the course.
7. Do I need a Google Cloud account?
Yes, instructions will be given for setting up a free GCP trial if needed.
8. Is this course suitable for business teams?
Yes, business teams will gain clarity on use cases and AI integration strategies.
9. Will course materials be shared?
Yes, all slides, lab guides, and code snippets will be provided.
10. Does this course support certification prep?
Yes, it’s aligned with Google Cloud’s ML Engineering certification path.
