What are the horizontal layers included in TFX for pipeline management and optimization?
TFX, which stands for TensorFlow Extended, is a comprehensive end-to-end platform for building production-ready machine learning pipelines. It provides a set of tools and components that facilitate the development and deployment of scalable and reliable machine learning systems. TFX is designed to address the challenges of managing and optimizing machine learning pipelines, enabling data scientists
What are the different phases of the ML pipeline in TFX?
The TensorFlow Extended (TFX) is a powerful open-source platform designed to facilitate the development and deployment of machine learning (ML) models in production environments. It provides a comprehensive set of tools and libraries that enable the construction of end-to-end ML pipelines. These pipelines consist of several distinct phases, each serving a specific purpose and contributing
What challenges must be addressed when putting a software application into production?
When putting a software application into production, there are several challenges that must be addressed to ensure a smooth and successful deployment. These challenges can arise from various aspects of the application, including its architecture, scalability, reliability, security, and performance. In the context of Artificial Intelligence (AI) and specifically TensorFlow Extended (TFX), there are additional
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What are the ML-specific considerations when developing an ML application?
When developing a machine learning (ML) application, there are several ML-specific considerations that need to be taken into account. These considerations are important in order to ensure the effectiveness, efficiency, and reliability of the ML model. In this answer, we will discuss some of the key ML-specific considerations that developers should keep in mind when
What is the purpose of TensorFlow Extended (TFX) framework?
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