Lilly Tech Systems
Learn Google Vertex AI
Master Google Cloud's unified machine learning platform. Build, train, and deploy ML models at scale using AutoML, custom training, and Vertex AI Pipelines — all within the Google Cloud ecosystem.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Google Vertex AI? Explore Google Cloud's unified ML platform, its evolution from AI Platform, and key capabilities.
2. Setup
Set up your GCP account, create a project, enable APIs, and configure Vertex AI Workbench for development.
3. Training Models
Train models with AutoML, custom training jobs, TPU acceleration, and hyperparameter tuning on Vertex AI.
4. Deployment
Deploy models to endpoints, configure online and batch predictions, and explore Model Garden for pre-trained models.
5. Pipelines
Build ML pipelines with Vertex AI Pipelines and Kubeflow, manage features with Feature Store, and use Model Registry.
6. Best Practices
Production tips, cost optimization, security, monitoring, and scaling strategies for Vertex AI workloads.
What You'll Learn
By the end of this course, you'll be able to:
Train ML Models
Use AutoML for no-code training or build custom training jobs with TensorFlow, PyTorch, and scikit-learn on Google Cloud.
Deploy & Serve
Deploy models to scalable endpoints with online and batch prediction, auto-scaling, and traffic splitting.
Build Pipelines
Orchestrate end-to-end ML workflows with Vertex AI Pipelines, Kubeflow components, and automated retraining.
Manage ML Ops
Implement MLOps best practices with Feature Store, Model Registry, model monitoring, and experiment tracking.