Vertex AI Workshop - Training, Serving, and Pipelines

Dive into a comprehensive workshop on Google Vertex AI with Sascha Heyer, Senior Machine Learning Engineer at DoiT. Learn how to train models using Vertex AI Training, serve models with Vertex AI Endpoints, and integrate these components using Vertex AI Pipelines. Explore the Model Registry and Experiments features while gaining hands-on experience with cloud-based machine learning. Discover the benefits of containerization, serverless pipelines, and batch prediction services. Gain insights into optimizing deployments, working with limitations, and understanding pricing structures. Perfect for those looking to enhance their MLOps skills and leverage Google Cloud's machine learning capabilities effectively.

[] Introduction to Sascha Heyer
[] Code, article, and videos
[] This episode's topics
[] Training ML Models
[] Training with Vertex AI
[] Training application
[] Why Container
[] Overall process
[] Demo
[] Vertex AI Training
[] Training Application
[] Enable monitoring for new versions of models
[] Using spot instances while kicking off the training jobs
[] Enabling Tensorflow real-time access while the job is traing
[] When to use Vertex AI vs when to use Google AI platform
[] Same components with Kubeflow
[] Control inside VPC
[] Serving ML Models
[] Different ways in Serving ML Models
[] Pre-build container for prediction
[] Custom container for prediction
[] Model serving steps
[] Upload model
[] Endpoint
[] Deploy model
[] Container requirements
[] Build customer container I
[] Build customer container II
[] Build customer container III
[] Getting predictions
[] Serving notebook demo
[] Optimizations around speeding up deployment
[] Working with Sagemaker relating to Vertex
[] Payload limitations
[] Limitations
[] Pricing
[] Machine Learning Teams don't need Kubernetes
[] Google Vertex AI Pipelines, a serverless product to run Kubeflow or TFX Pipelines
[] Vertex Pipelines and Kubeflow
[] Basic Pipeline
[] Required Modules
[] Components
[] Compiler
[] Demo
[] Component types
[] Predefined components
[] Component Specification
[] Share Components
[] Parameters
[] Model Lineage
[] Using Vertex Experiments
[] Scheduling pipelines
[] Production models trained and deploy
[] Vertex Batch prediction Service
[] Batch predictions are useful
[] Wrap up