TensorFlow Extended (TFX)

TFX concepts

TFX pipeline nodes

TFX standard data components

TFX standard model components

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