TensorFlow Extended (TFX)
May 1, 2024
Updated June 29, 2025
11 minute read
TensorFlow Extended (TFX) is an end-to-end platform for developing, orchestrating, and monitoring machine learning pipelines. Designed by Google, TFX simplifies the process of building, deploying, and managing ML models by providing a comprehensive set of tools and features. Whether you're a data scientist, a machine learning engineer, or a software developer, TFX can empower you to create and deploy ML solutions with confidence and efficiency.
What is TensorFlow Extended (TFX)?
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Find a path to becoming a TensorFlow Extended (TFX). Learn more at:
OpenCourser.com/topic/kq92dt/tensorflow
Reading list
We've selected 28 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
TensorFlow Extended (TFX).
Is specifically focused on building machine learning pipelines using the TensorFlow ecosystem, making it highly relevant to TFX. It provides a practical guide to automating the ML lifecycle and is valuable for understanding how TFX components fit together in a production setting. It serves as a strong foundational text and a useful reference for practitioners.
This comprehensive tutorial provides a thorough overview of TFX, covering all aspects of ML pipeline development and deployment. It is suitable for practitioners with some experience in ML and TensorFlow who want to gain a deeper understanding of TFX.
Offers a broad introduction to MLOps principles and practices, which are essential for understanding the purpose and application of TFX. It covers topics like CI/CD for ML, infrastructure automation, and monitoring, providing valuable context for why TFX necessary tool in production ML. It good resource for gaining a broad understanding of the field TFX operates within.
While not exclusively about TFX, this book provides a holistic view of designing production-ready ML systems. It covers crucial aspects like data-centric AI, deployment, and monitoring, which are directly addressed by TFX components. is excellent for deepening the understanding of the challenges TFX aims to solve and the best practices in the field.
Focuses on advanced techniques and best practices for building and deploying ML pipelines with TFX. It covers topics such as pipeline optimization, data versioning, and monitoring.
Given the course names mentioning Google Cloud Vertex Pipelines, this book is highly relevant as Vertex AI platform that supports TFX. It covers MLOps best practices within the Google Cloud ecosystem, providing practical knowledge for deploying TFX on Vertex AI.
This practical guide focuses on building and deploying ML pipelines using TFX. It provides step-by-step instructions and real-world examples to help practitioners implement TFX in their own projects.
Explores reusable solutions for common ML challenges, including those in MLOps. Understanding these patterns can help in designing more effective and robust TFX pipelines. It provides valuable insights into best practices and common pitfalls in production ML.
Provides guidance on operationalizing ML models and team collaboration in an enterprise setting. It covers MLOps basics and the ML lifecycle, offering practical examples. Published in 2020, it remains relevant for understanding the foundational concepts that TFX builds upon and is suitable for intermediate to advanced practitioners.
Provides a production-first perspective on implementing MLOps within an enterprise. It covers the practicalities of building and deploying ML systems at scale, offering insights relevant to organizations adopting TFX for production use.
This step-by-step tutorial provides a hands-on introduction to TFX. It takes readers through the process of building and deploying a complete ML pipeline, covering data ingestion, feature engineering, model training, and evaluation.
Provides a practical introduction to TFX, focusing on the core concepts and components of ML pipelines. It is suitable for practitioners with some experience in ML and TensorFlow who want to learn about TFX.
Offers a code-centric introduction to ML engineering and covers the model development process, deployment patterns, and tools relevant to MLOps. It provides practical implementation examples in Python, which is the primary language for TFX. This book is valuable for understanding the engineering practices that complement the use of TFX.
Provides a case study-based approach to building and deploying ML pipelines with TFX. It walks readers through real-world examples of ML pipelines in various domains.
Focuses on implementing efficient and scalable ML systems using cloud services, which is highly relevant for deploying TFX pipelines on platforms like Google Cloud. It covers setting up serverless ML infrastructure and using MLOps tools, providing practical knowledge for scaling TFX implementations.
Given that TFX pipelines can be orchestrated on platforms like Kubeflow, which runs on Kubernetes, this book provides essential background knowledge on using Kubernetes for ML workloads. It covers scaling ML systems and integrating with MLOps practices, offering valuable context for deploying TFX pipelines in a production environment.
Applies Site Reliability Engineering (SRE) principles to ML in production, focusing on building reliable and maintainable ML systems. Reliability key outcome of using frameworks like TFX, making this book valuable for understanding the operational excellence goals that TFX supports.
Data engineering critical prerequisite for effective TFX implementation. focuses on building and managing data pipelines specifically for AI/ML projects. It covers foundational concepts, data lifecycle, and architecting data solutions, providing necessary background for working with TFX's data-related components.
A strong understanding of data engineering is foundational for working with TFX, as TFX pipelines heavily rely on well-engineered data inputs. provides a comprehensive guide to planning and building robust data systems, serving as essential background reading.
Focuses on the entire process of building and deploying ML-powered applications. It covers the steps from ideation to production, providing a valuable product-oriented perspective that complements the pipeline focus of TFX. It helps understand the broader context in which TFX is used.
Given that Airflow is mentioned as an orchestration tool for TFX pipelines, this book is highly relevant for understanding how to build and manage data workflows using Airflow. It provides practical guidance on a tool commonly used in conjunction with TFX.
Addresses the practical aspects of taking deep learning models to production, including designing systems, structuring code, data processing pipelines, and deployment. While not TFX-specific, it covers essential MLOps concepts and challenges that TFX helps to solve.
Covers the process of moving ML models from experimentation to production, including developing and optimizing workflows. It addresses many of the practical challenges that TFX is designed to mitigate, making it a relevant read for understanding the production ML landscape.
Delves into TensorFlow 2 and covers building and deploying deep learning models. It touches upon using TFX for easy production pipelines, providing a TensorFlow-centric view that complements TFX. It's useful for those wanting to see how TFX integrates with core TensorFlow development.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/kq92dt/tensorflow
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