From Experimentation to Products: The Production Machine Learning Journey • Robert Crowe • GOTO 2021

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This presentation was recorded at GOTOpia February 2021. #GOTOcon #GOTOpia

Robert Crowe - TensorFlow Developer Advocate at Google

A machine learning (ML) journey typically starts with trying to understand the world, and looking for data that describes it. This leads to an experimentation phase, where we try to use that data to model the parts of the world that we’re interested in, often because they directly affect our users or our business. Once we have one or more models that deliver good results, it’s time to move those models into production.
Deploying advanced machine learning technology to serve customers and/or business needs requires a rigorous approach and production-ready systems. This is especially true for maintaining and improving model performance over the lifetime of a production application. Unfortunately, the issues involved and approaches available are often poorly understood.
A ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed using tools and systems which suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components.
In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments.
In this talk, Robert will discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow.
Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology [...]

00:00 Intro
02:15 Production ML
05:41 We need MLOps
06:21 Continuous integration, deployment and testing
07:29 MLOps level 0: Manual Process
09:02 Experiment
12:11 Tales from the trenches
13:02 TensorFlow Extended (TFX)
14:28 TFX production components
16:43 What is a TFX component?
18:20 TFX orchestration
19:16 Difference between TFX & Kubeflow pipelines
23:00 Distributed pipeline processing: Apache Beam
25:28 TFX standard components
25:53 Components: ExampleGen, StatisticsGen & SchemaGen
28:17 Components: ExampleValidator, Transform & Trainer
31:45 Components: Tuner, Evaluator & InfraValidator
32:51 Components: Pusher & BulkInferrer
33:37 TFX pipeline nodes
34:43 TRFX custom components
36:09 Very high level architecture
37:03 Outro

Download slides and read the full abstract here:
#MachineLearning #ML #TensorFlow #TF #TFX #TensorFlowExtended #Kubeflow #AI #ArtificialIntelligence #DataScience #MLOps #CI #ContinuousIntegration #Testing #Orchestration #ApacheBeam #ExampleGen #StatisticsGen #SchemaGen

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