TensorFlow

TensorFlow TFX

Composite Score

8.2 /10

CX Score

8.5 /10

15 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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2024 Data Quadrant Gold Medalist

Latest Award, 2024

8 Top Rated Capabilities

10 Top Rated Features

Latest Award, 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

1
Since last award

100 Plan to Renew

87 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.

+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

Algorithm Recommendation

Ensembling

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

Ashay S.

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

Validated Review

Verified Reviewer

Submitted Jan 2023

Great for Designing End to End ML pipelines

Likeliness to Recommend

10 /10

What differentiates TensorFlow TFX from other similar products?

The best part about TFX is since it is built on top of TensorFlow which many are familiar with it makes it easier to use due to having similar syntax and features. It also helps integrate it with TensorFlow's data integration and validation tools.

What is your favorite aspect of this product?

TFX integrates with GCP, making model deployment to Cloud AI Platform and Cloud ML Engine simple .GCP integration offers built-in integration with BigQuery, Dataflow, and Bigtable. Versioning, rollback, and monitoring in TFX help manage model deployments and rollbacks in case of problems. TFX lets data scientists and ML developers track model performance over time and learn how to improve it.

What do you dislike most about this product?

It might be daunting to start with it for beginners but as an experienced data scientist I have so far have no major dislikes with this.

What recommendations would you give to someone considering this product?

If you're looking for a top-tier MLOps solution that's also straightforward to integrate with Google's top-tier ML services, look no further. It has the potential to be a helpful tool for automating the many pipelines used in the development process, which in turn may save both time and money.

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

TensorFlow TFX

What is TensorFlow TFX?

Company Details

Need Assistance?

Awards & Recognition

2024 Data Quadrant Gold Medalist

TensorFlow TFX Ratings

92 Likeliness to Recommend

1 Since last award

100 Plan to Renew

87 Satisfaction of Cost Relative to Value

1 Since last award

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.

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

Ensembling 'Ability to combine ML models for best results.

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

Ashay S.

Great for Designing End to End ML pipelines

Likeliness to Recommend

Pros

Most Popular TensorFlow TFX Comparisons

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1
Since last award

1
Since last award

Feature Ratings

Data Labeling

Algorithm Diversity

Feature Engineering

Performance and Scalability

Data Exploration and Visualization

Model Monitoring and Management

Algorithm Recommendation

Ensembling

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