Vertex AI: 1 Day Foundations Course in Seattle, WA

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Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

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.

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

• Machine learning practitioners
• Data scientists
• ML engineers
• Developers
• Business teams

Google's Vertex AI consolidates the full lifecycle of machine learning into one intuitive platform—and this course helps you unlock its full potential. Whether you're creating fast prototypes or scaling enterprise-grade AI systems, you’ll gain the hands-on skills and practical insights needed to innovate efficiently. Our instructors provide cloud-native best practices that bridge the gap between development and production, enabling you to build AI solutions that deliver measurable business value.

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Looking to empower your whole team with cloud AI skills?

We offer in-house training sessions that are fully customizable to your industry, tech stack, and project needs. Want to train your data teams on lifelike generative AI or streamline your MLOps workflow? Let us tailor a hands-on program that accelerates innovation within your organization.

📧 Contact us today to schedule a customized in-house session:

Info:
• What is Vertex AI and why it matters
• Key components and capabilities
• Vertex AI vs other ML platforms
• Walkthrough: Vertex AI dashboard

Info:
• Importing and labeling datasets
• Preparing structured and unstructured data
• Managing data in Google Cloud Storage
• Exercise

Info:
• When to use AutoML vs custom models
• Creating and training AutoML models
• Use cases: image, text, tabular data
• Activity

Info: • Notebook environments and custom code
• Training jobs and resource configuration
• Model registry and versioning
• Training

Info:
• Performance metrics and evaluation tools
• Model explainability features in Vertex AI
• Debugging ML pipelines
• Case study

Info:
• Introduction to MLOps on GCP
• Pipelines, CI/CD, and workflow automation
• Artifact management and monitoring
• Activity

Info:
• Deployment options: online & batch predictions
• Autoscaling and A/B testing setups
• Monitoring model drift and performance
• Exercise

Info:
• Using pre-built generative AI models
• Fine-tuning with your own data
• Use cases: chatbots, content generation, search
• Hands-on activity

Info:
• AI across industries: retail, healthcare, finance
• Governance and ethical AI considerations
• Cost optimization strategies on GCP
• Group discussion

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