What is the purpose of TensorFlow Extended (TFX) framework?
Sunday, 06 August 2023
by EITCA Academy
The purpose of TensorFlow Extended (TFX) framework is to provide a comprehensive and scalable platform for the development and deployment of machine learning (ML) models in production. TFX is specifically designed to address the challenges faced by ML practitioners when transitioning from research to deployment, by providing a set of tools and best practices for
What are the standard components of TFX for building production-ready ML pipelines?
Saturday, 05 August 2023
by EITCA Academy
TFX (TensorFlow Extended) is a powerful open-source framework developed by Google for building production-ready machine learning (ML) pipelines. It provides a set of standard components that enable ML engineers to efficiently develop, deploy, and maintain ML models in a scalable and reproducible manner. In this answer, we will explore the key components of TFX and
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow Extended (TFX), ML engineering for production ML deployments with TFX, Examination review
Tagged under:
Artificial Intelligence, Machine Learning, ML Pipelines, Production Deployment, TensorFlow, TFX