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From Experimentation to Products - The Production Machine Learning Journey

GOTO Conferences via YouTube

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Overview

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This talk examines how to build production machine learning applications with pipeline architectures, drawing on Google’s experience with TensorFlow Extended (TFX). It reviews TFX components and orchestration, compares TFX with Kubeflow Pipelines, and discusses distributed processing with Apache Beam.

Syllabus

Intro
Production ML
We need MLOps
Continuous integration, deployment and testing
MLOps level 0: Manual Process
Experiment
Tales from the trenches
TensorFlow Extended TFX
TFX production components
What is a TFX component?
TFX orchestration
Difference between TFX & Kubeflow pipelines
Distributed pipeline processing: Apache Beam
TFX standard components
Components: ExampleGen, StatisticsGen & SchemaGen
Components: ExampleValidator, Transform & Trainer
Components: Tuner, Evaluator & InfraValidator
Components: Pusher & BulkInferrer
TFX pipeline nodes
TRFX custom components
Very high level architecture
Outro

Taught by

GOTO Conferences

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