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TensorFlow

TensorFlow TFX

Composite Score

8.3 /10

CX Score

8.6 /10

27 Reviews Write a Review

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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.

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Awards & Recognition

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

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2025 Data Quadrant Champion

Latest Award, 2025

6 Top Rated Capabilities

4 Top Rated Features

Latest Award, 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

100 Plan to Renew

85 Satisfaction of Cost Relative to Value

1
Since last award

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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?

0% Negative

4% Neutral

96% Positive

Pros

Continually Improving Product
Trustworthy
Efficient Service
Caring

Feature Ratings

Average 83

Feature Engineering

Algorithm Diversity

Performance and Scalability

Data Labeling

Model Monitoring and Management

Model Training

Model Tuning

Openness and Flexibility

Data Pre-Processing

Ensembling

Data Exploration and Visualization

Vendor Capability Ratings

Average 81

Quality of Features

Breadth of Features

Ease of Customization

Availability and Quality of Training

Business Value Created

Product Strategy and Rate of Improvement

Ease of IT Administration

Ease of Implementation

Ease of Data Integration

Usability and Intuitiveness

Vendor Support

TensorFlow TFX Reviews

Jefferson A.

Role: Industry Specific Role
Industry: Engineering
Involvement: IT Development, Integration, and Administration

Validated Review

Verified Reviewer

Submitted Jan 2025

"Powerful tool!"

Likeliness to Recommend

10 /10

What differentiates TensorFlow TFX from other similar products?

TensorFlow TFX differentiates itself with its end-to-end ML pipeline automation, seamless integration with TensorFlow, and production-ready tools like model validation, monitoring, and versioning. Its scalability supports distributed training and deployment across cloud and on-prem environments, while built-in components streamline workflows. Unlike many frameworks, TFX is designed specifically for moving machine learning from experimentation to reliable, scalable production systems.

What is your favorite aspect of this product?

For me its Scalability – TensorFlow TFX excels in handling large-scale machine learning workflows, allowing seamless model training, deployment, and monitoring in production environments. Its ability to scale efficiently across distributed systems is a key strength.

What do you dislike most about this product?

For me its the steep learning curve – While powerful, TensorFlow TFX can be difficult for newcomers to navigate due to its complexity and the need for familiarity with TensorFlow's ecosystem, which can slow down initial adoption.

What recommendations would you give to someone considering this product?

Master TensorFlow first – Familiarity with TensorFlow is essential for leveraging TFX effectively. Prepare for a learning curve – Be ready to invest time in understanding TFX's components and pipeline setup. Leverage community support – Use the extensive documentation and forums to troubleshoot and optimize workflows.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Niharika M.

Role: Information Technology
Industry: Engineering
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Mar 2024

Easy to deploy large model

Likeliness to Recommend

10 /10

What differentiates TensorFlow TFX from other similar products?

the deployment part we can deploy large ml model in this and easy to use

What is your favorite aspect of this product?

the machine learning training and deployment part and also training part

What do you dislike most about this product?

nothing to dislike easy to use and large resources

What recommendations would you give to someone considering this product?

i'll recommend to everyone use it once

Pros

Helps Innovate
Continually Improving Product
Performance Enhancing
Enables Productivity

Ranjeet M.

Role: Sales Marketing
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Dec 2023

Fantastic product

Likeliness to Recommend

10 /10

What differentiates TensorFlow TFX from other similar products?

felxiblity ,easy to use, High level apis, it supposr various platform,it support for research and production,

What is your favorite aspect of this product?

Easy to use and sclable ,the most good part is taht it support multiple platform

What do you dislike most about this product?

Nothing as of dislike

What recommendations would you give to someone considering this product?

ITs open soures machine learning framework that make work easy, IT support multiple platform

Pros

Helps Innovate
Reliable
Performance Enhancing
Enables Productivity

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

100 Plan to Renew

85 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

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

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

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

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

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

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

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

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

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.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

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.

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.

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.

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.

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

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.

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.

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

Jefferson A.

"Powerful tool!"

Likeliness to Recommend

Pros

Niharika M.

Easy to deploy large model

Likeliness to Recommend

Pros

Ranjeet M.

Fantastic product

Likeliness to Recommend

Pros

Most Popular TensorFlow TFX Comparisons

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DataRobot AI Platform

1
Since last award

Feature Ratings

Feature Engineering

Algorithm Diversity

Performance and Scalability

Data Labeling

Model Monitoring and Management

Model Training

Model Tuning

Openness and Flexibility

Data Pre-Processing

Ensembling

Data Exploration and Visualization

Vendor Capability Ratings

Quality of Features

Breadth of Features

Ease of Customization

Availability and Quality of Training

Business Value Created

Product Strategy and Rate of Improvement

Ease of IT Administration

Ease of Implementation

Ease of Data Integration

Usability and Intuitiveness

Vendor Support