Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventuality. Practices like testing and reliable technologies help make Software Engineering reliable enough to build industries upon. Meanwhile, Machine Learning (ML) has also grown over the past 2+ decades. ML is used more a
Get Started with TensorFlow Transform | TFX
Deploy ML on mobile, microcontrollers and other edge devices
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Two approaches for data validation in ML production
GitHub - google/ml-metadata: For recording and retrieving metadata associated with ML developer and data scientist workflows.
Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build | Cloud Architecture Center | Google Cloud
From Research to Production with TFX Pipelines and ML Metadata
Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform
TensorFlow Extended (TFX) (TensorFlow Dev Summit 2018)
