Towards ML Engineering with TensorFlow Extended (TFX)
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The discipline of Software Engineering has evolved over the past 5+ decades to good levels of maturity. This maturity is in fact both a blessing and a necessity, since the modern world largely depends on it.
At the same time, the popularity of Machine Learning (ML) has been steadily increasing over the past 2+ decades, and over the last decade ML is being increasingly used for both experimentation and production workloads. It is no longer uncommon for ML to power widely used applications and products that are integral parts of our life. Much like what was the case for Software Engineering, the proliferation of use of ML technology necessitates the evolution of the ML discipline from “Coding” to “Engineering”.
Gus Katsiapis offers a view from the trenches of using and building end-to-end ML platforms, and shares collective knowledge and experience, gothered over more than a decade of applied ML at Google. We hope this helps pave the way towards a world of ML Engineering.
Kevin Haas offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet (and beyond). TFX helps effectively manage the end-to-end training and production workflow including model management, versioning, and serving, thereby helping one realize aspects of ML Engineering.
This is a joint talk with Kevin Haas.
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