This content is currently locked.

Your current Info-Tech Research Group subscription does not include access to this content. Contact your account representative to gain access to Premium SoftwareReviews.

Contact Your Representative

Or Call Us:
1-888-670-8889 (US/CAN) or
+1-519-432-3550 (International)

TensorFlow

TensorFlow TFX

Composite Score

8.2 /10

CX Score

8.5 /10

15 Reviews Write a Review

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

2024 Emotional Footprint Champion

Latest Award, 2024

Strategy and Innovation
Service Experience
Product Impact
Conflict Resolution

Latest Award, 2024

2024 Data Quadrant Gold Medalist

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.

92 Likeliness to Recommend

100 Plan to Renew

87 Satisfaction of Cost Relative to Value

{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.

+97 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

0% Neutral

100% Positive

Pros

Helps Innovate
Continually Improving Product
Reliable
Enables Productivity

Feature Ratings

Average 86

Data Labeling

Algorithm Diversity

Feature Engineering

Performance and Scalability

Data Exploration and Visualization

Model Monitoring and Management

Ensembling

Algorithm Recommendation

Pre-Packaged AI/ML Services

Model Tuning

Data Pre-Processing

Vendor Capability Ratings

Average 83

Quality of Features

Business Value Created

Breadth of Features

Ease of IT Administration

Ease of Customization

Product Strategy and Rate of Improvement

Availability and Quality of Training

Ease of Implementation

Vendor Support

Usability and Intuitiveness

Ease of Data Integration

TensorFlow TFX Reviews

Role: Student Academic
Industry: Education
Involvement: End User of Application

Validated Review

Anonymous Reviewer

Submitted Sep 2023

Wonderful very easy to use product

Likeliness to Recommend

9 /10

Pros

Helps Innovate
Reliable
Enables Productivity
Trustworthy

Cons

Vendor Friendly Policies

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

Latest Research

TensorFlow TFX

What is TensorFlow TFX?

Company Details

Need Assistance?

Awards & Recognition

2024 Emotional Footprint Champion

2024 Data Quadrant Gold Medalist

TensorFlow TFX Ratings

92 Likeliness to Recommend

100 Plan to Renew

87 Satisfaction of Cost Relative to Value

Emotional Footprint Overview

+97 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

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

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

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

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

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

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

Ensembling 'Ability to combine ML models for best results.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

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

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

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.

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.

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

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.

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.

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.

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.

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.

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

TensorFlow TFX Reviews

Wonderful very easy to use product

Likeliness to Recommend

Pros

Cons

Mohd K.

TFX: A Production-Ready Machine Learning Platform

Likeliness to Recommend

Pros

Shaurya S.

Production Grade machine learning pipelines

Likeliness to Recommend

Pros

Most Popular TensorFlow TFX Comparisons

Google Cloud Vertex AI

MathWorks MATLAB

AWS Machine Learning

Microsoft Azure Machine Learning

DataRobot AI Platform

Databricks Data Intelligence Platform

Title

Feature Ratings

Data Labeling

Algorithm Diversity

Feature Engineering

Performance and Scalability

Data Exploration and Visualization

Model Monitoring and Management

Ensembling

Algorithm Recommendation

Pre-Packaged AI/ML Services

Model Tuning

Data Pre-Processing

Vendor Capability Ratings

Quality of Features

Business Value Created

Breadth of Features

Ease of IT Administration

Ease of Customization

Product Strategy and Rate of Improvement

Availability and Quality of Training

Ease of Implementation

Vendor Support

Usability and Intuitiveness

Ease of Data Integration