Write a review temporarily disabled. Our system flagged this request as potentially automated and has disabled form submission for security reasons. If you believe this is a mistake, please contact us. We’ll be happy to help resolve the issue.

TensorFlow

TensorFlow TFX

Composite Score

8.3 /10

CX Score

8.6 /10

32 Reviews Write a Review

Claim Software

What is TensorFlow TFX?

TFX is an end-to-end platform for deploying production ML pipelines. A TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually. When you're ready to move your models from research to production, TFX can be used to create and manage a production pipeline.

Company Details

Need Assistance?

We're here to help you with understanding our reports and the data inside to help you make decisions.

Get Assistance

Awards & Recognition

TensorFlow TFX won the following awards in the Machine Learning Platforms category

Filter By

Contract Negotiation

Latest Award, 2025

2025 Data Quadrant Champion

2025

6 Top Rated Capabilities

4 Top Rated Features

2025

2024 Emotional Footprint Champion

2024

Strategy and Innovation
Service Experience
Product Impact
Conflict Resolution

2024

2024 Data Quadrant Champion

2024

8 Top Rated Capabilities

10 Top Rated Features

2024

TensorFlow TFX Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on TensorFlow TFX.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

89 Likeliness to Recommend

1
Since last award

100 Plan to Renew

84 Satisfaction of Cost Relative to Value

1
Since last award

{y}

{name}

Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+93 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love TensorFlow TFX?

4% Negative

3% Neutral

93% Positive

Pros

Continually Improving Product
Trustworthy
Efficient Service
Caring

Feature Ratings

Average 81

Performance and Scalability

Model Training

Feature Engineering

Data Labeling

Openness and Flexibility

Algorithm Diversity

Model Tuning

Model Monitoring and Management

Data Pre-Processing

Ensembling

Explainability

Vendor Capability Ratings

Average 80

Quality of Features

Availability and Quality of Training

Business Value Created

Ease of IT Administration

Product Strategy and Rate of Improvement

Ease of Customization

Breadth of Features

Usability and Intuitiveness

Ease of Implementation

Ease of Data Integration

Vendor Support

TensorFlow TFX Reviews

Mohd K.

Role: Information Technology
Industry: Technology
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Jul 2023

TFX: A Production-Ready Machine Learning Platform

Likeliness to Recommend

9 /10

What differentiates TensorFlow TFX from other similar products?

TensorFlow Extended (TFX) is a production-ready machine learning platform that is designed to help you automate the ML process, from data preparation to model deployment. It provides a number of features that differentiate it from other similar products, including: A set of modular components A configuration framework Support for multiple ML framework

What is your favorite aspect of this product?

My favorite aspect of TensorFlow Extended (TFX) is its modularity. The platform is made up of a set of individual components that can be used to build ML pipelines. This makes it easy to customize your pipelines to meet your specific needs.

What do you dislike most about this product?

My biggest dislike about TensorFlow Extended (TFX) is its steep learning curve. The platform is complex and there is a lot to learn in order to use it effectively.

Pros

Performance Enhancing
Unique Features
Fair
Acts with Integrity

Shaurya S.

Role: Information Technology
Industry: Finance
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Jul 2023

Production Grade machine learning pipelines

Likeliness to Recommend

9 /10

What differentiates TensorFlow TFX from other similar products?

Functional API requiring little configuration for production grade code, which is reliable and efficient.

What is your favorite aspect of this product?

Tight integration with GCP means there are additional plugins that can be used for making this.

What do you dislike most about this product?

The variety of models offered out of the box leave something to be desired.

What recommendations would you give to someone considering this product?

A good command on the open source Tensorflow library is needed for efficient usage of TFX

Pros

Effective Service
Appreciates Incumbent Status
Helps Innovate
Enables Productivity

Muskan H.

Role: Industry Specific Role
Industry: Engineering
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Jun 2023

Production-Ready Pipelines

Likeliness to Recommend

10 /10

What differentiates TensorFlow TFX from other similar products?

Some key differentiating features of TensorFlow TFX: 1. TensorFlow TFX (TensorFlow Extended) is a comprehensive platform for building and deploying production-ready machine learning (ML) pipelines. 2. TFX is specifically designed for building ML pipelines that are scalable, modular, and production-ready. It provides components for data validation, preprocessing, model training, evaluation, and serving, enabling the creation of end-to-end pipelines that can be deployed in real-world production environments.

What is your favorite aspect of this product?

TFX is a repository for managing and versioning ML models. TFX is designed to handle large-scale ML workflows efficiently.

What do you dislike most about this product?

TFX's extensive set of tools and components may introduce unnecessary complexity and overhead for smaller ML projects or experiments. Users might encounter difficulties finding detailed examples or troubleshooting specific issues related to TFX.

What recommendations would you give to someone considering this product?

TensorFlow integration, and scalable capabilities, making it an excellent choice for building end-to-end ML pipelines in real-world applications.

Pros

Client Friendly Policies
Helps Innovate
Continually Improving Product
Reliable

TensorFlow TFX

What is TensorFlow TFX?

Company Details

Need Assistance?

Awards & Recognition

2025 Data Quadrant Champion

2024 Emotional Footprint Champion

2024 Data Quadrant Champion

TensorFlow TFX Ratings

89 Likeliness to Recommend

1 Since last award

100 Plan to Renew

84 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+93 Net Emotional Footprint

How much do users love TensorFlow TFX?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

Model Training Ability to train a model with a range of data sets that you provide.

Feature Engineering Ability to extract and build features from raw data.

Data Labeling Ability to annotate/ label data to create a training data set.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Model Tuning Ability to fine-tune models based on monitoring feedback within the platform.

Model Monitoring and Management Ability to monitor model use and performance in production, incl. identifying models for refresh and/or replacement.

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Ensembling 'Ability to combine ML models for best results.

Explainability Ability to explain/ visualize the model.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

TensorFlow TFX Reviews

Mohd K.

TFX: A Production-Ready Machine Learning Platform

Likeliness to Recommend

Pros

Shaurya S.

Production Grade machine learning pipelines

Likeliness to Recommend

Pros

Muskan H.

Production-Ready Pipelines

Likeliness to Recommend

Pros

Most Popular TensorFlow TFX Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

AWS Machine Learning

Microsoft Azure Machine Learning

Databricks Data Intelligence Platform

DataRobot AI Platform

1
Since last award

1
Since last award

Feature Ratings

Performance and Scalability

Model Training

Feature Engineering

Data Labeling

Openness and Flexibility

Algorithm Diversity

Model Tuning

Model Monitoring and Management

Data Pre-Processing

Ensembling

Explainability

Vendor Capability Ratings

Quality of Features

Availability and Quality of Training

Business Value Created

Ease of IT Administration

Product Strategy and Rate of Improvement

Ease of Customization

Breadth of Features

Usability and Intuitiveness

Ease of Implementation

Ease of Data Integration

Vendor Support