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1
Intro
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Production ML
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We need MLOps
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Continuous integration, deployment and testing
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MLOps level 0: Manual Process
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Experiment
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Tales from the trenches
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TensorFlow Extended TFX
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TFX production components
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What is a TFX component?
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TFX orchestration
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Difference between TFX & Kubeflow pipelines
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Distributed pipeline processing: Apache Beam
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TFX standard components
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Components: ExampleGen, StatisticsGen & SchemaGen
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Components: ExampleValidator, Transform & Trainer
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Components: Tuner, Evaluator & InfraValidator
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Components: Pusher & BulkInferrer
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TFX pipeline nodes
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TRFX custom components
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Very high level architecture
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Outro
Description:
Explore the journey from machine learning experimentation to production in this comprehensive conference talk. Dive into the world of MLOps and learn about the challenges and solutions in deploying advanced ML technology. Discover the importance of continuous integration, deployment, and testing in ML applications. Gain insights into TensorFlow Extended (TFX) and its production components, understanding how it scales for large-scale ML applications. Compare TFX with Kubeflow pipelines and explore distributed pipeline processing using Apache Beam. Examine various TFX standard components, including ExampleGen, StatisticsGen, SchemaGen, and more. Learn about TFX pipeline nodes, custom components, and high-level architecture. Perfect for data scientists, ML engineers, and professionals looking to enhance their knowledge of production-ready ML systems and best practices in MLOps.

From Experimentation to Products - The Production Machine Learning Journey

GOTO Conferences
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