How does TFX leverage Apache Beam in ML engineering for production ML deployments?
Apache Beam is a powerful open-source framework that provides a unified programming model for both batch and streaming data processing. It offers a set of APIs and libraries that enable developers to write data processing pipelines that can be executed on various distributed processing backends, such as Apache Flink, Apache Spark, and Google Cloud Dataflow.
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow Extended (TFX), ML engineering for production ML deployments with TFX, Examination review
What role does metadata play in TFX pipelines?
Metadata plays a crucial role in TFX (TensorFlow Extended) pipelines, serving as a vital component for managing and tracking the various stages of the machine learning (ML) engineering process. In the context of TFX, metadata refers to the information about the data, models, and pipeline components that are used during the ML workflow. This metadata
How does TFX address the challenges posed by changing ground truth and data in ML engineering for production ML deployments?
TFX (TensorFlow Extended) is a powerful framework that addresses the challenges posed by changing ground truth and data in ML engineering for production ML deployments. It provides a comprehensive set of tools and best practices to handle these challenges effectively and ensure the smooth operation of ML models in production. One of the key challenges
What are the three types of production ML scenarios based on the rate of change in ground truth and data?
In the field of machine learning (ML) engineering for production ML deployments with TensorFlow Extended (TFX), there are three types of production ML scenarios based on the rate of change in ground truth and data. These scenarios are known as static, dynamic, and evolving ML scenarios. 1. Static ML Scenarios: In a static ML scenario,
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow Extended (TFX), ML engineering for production ML deployments with TFX, Examination review