This course helps participants understand end-to-end ML workflows, AutoML capabilities, model deployment, and responsible AI practices using a unified AI development environment.
Overview
Google Vertex AI Essentials Training provides a practical introduction to building, training, deploying, and managing machine learning models using Google Cloud’s Vertex AI platform. This course helps participants understand end-to-end ML workflows, AutoML capabilities, model deployment, and responsible AI practices using a unified AI development environment.
Learning Outcomes
• Understand the fundamentals and core services of Vertex AI
• Learn how to build, train, and deploy AI/ML models using Vertex AI workflows
• Explore Generative AI, AutoML, and MLOps capabilities within Vertex AI
• Integrate AI models with cloud-based applications and data services
• Apply AI development best practices for scalability, monitoring, and automation
• Understand security, governance, and responsible AI practices in cloud AI environments
Duration & Delivery Mode
14 hours
Target Audience
• Data analysts and aspiring data scientists
• Machine learning engineers and developers
• Cloud architects and Google Cloud users
• AI and digital transformation teams
• Technical managers overseeing AI initiatives
Pre-requisites
• Basic understanding of machine learning or AI concepts
• Familiarity with cloud computing fundamentals
• Experience with Python or data tools is helpful but not mandatory
Skillset Achieved
• Understanding Vertex AI architecture and components
• Building and training ML models using Vertex AI
• Using AutoML for rapid model development
• Deploying and managing models on Google Cloud
• Applying responsible and ethical AI practices
Course Outcome
By the end of this training, participants will be able to build, train, deploy, and manage machine learning models using Google Vertex AI while following best practices for scalability, security, and responsible AI.
Course Outline
Introduction to Google Vertex AI
• Overview of Vertex AI and its unified ML platform
• Core components and services of Vertex AI
• Vertex AI vs traditional ML workflows
Data Preparation and Model Development
• Data ingestion and preprocessing in Vertex AI
• Working with datasets and feature engineering
• Training custom models using notebooks
AutoML with Vertex AI
• AutoML for tabular, vision, and text data
• Selecting algorithms and evaluation metrics
• Model optimization and performance tuning
Model Deployment and Predictions
• Deploying models to endpoints
• Online and batch prediction workflows
• Monitoring model performance
MLOps and Model Lifecycle Management
• Versioning, pipelines, and automation
• Managing experiments and reproducibility
• Scaling ML workflows in production
Responsible AI and Security
• Bias detection and fairness considerations
• Explainable AI using Vertex AI tools
• Security, compliance, and governance
Assessment Topics
• Fundamentals and architecture of Vertex AI
• AI/ML model development and deployment workflows
• AutoML, Generative AI, and MLOps concepts
• Data integration and cloud AI service utilization
• Security, monitoring, and responsible AI practices
• Practical hands-on Vertex AI implementation exercises
Evaluation
• Hands-on exercises using Vertex AI
• Model training and deployment tasks
• Final practical assessment
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants will receive an AcadNXT Certification in Google Vertex AI Essentials Training, validating their foundational skills in cloud-based machine learning and AI model management.
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What Our Students Say
“A clear and practical introduction to Vertex AI.”
“The AutoML sessions were especially valuable.”
“Excellent balance of theory and hands-on practice.”
“This course made Vertex AI easy to understand.”
“Highly relevant for teams adopting Google Cloud AI.”