Course Introduction
Course Content
0 / 34 completedTensorFlow Extended (TFX)
TFX concepts
TFX pipeline nodes
TFX standard data components
TFX standard model components
TFX libraries
Course Introduction
Apache Beam
TFX Orchestrators
TFX on Cloud AI Platform
TFX custom components - Python functions
TFX custom components - containers + subclassed
CICD for TFX pipeline workflows
TFX ML Metadata data model
TFX Pipeline Metadata
Containerized Training Applications
Containerizing PyTorch, Scikit, and XGBoost Applicatio
KubeFlow & AI Platform Pipelines
Continuous Training
What is Cloud Composer
Continuous Training Pipelines using Cloud Composer (model)
Apache Airflow, Containers, and TFX
Core Concepts of Apache Airflow
Continuous Training Pipelines using Cloud Composer (data)
Introduction
How MLflow tackles these challenges
Overview of ML development challenges
MLflow projects
MLflow tracking
MLflow models
Demo - Introduction
MLflow model registry
Deploying MLflow Locally Tracking Keras, TensorFlow, and Sckit-learn experiments
Course Summary