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 AssistanceTensorFlow 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
Down
3
Since last award
100 Plan to Renew
84 Satisfaction of Cost Relative to Value
Down
3
Since last award
Emotional Footprint Overview
- Product Experience:
- 90%
- Negotiation and Contract:
- 93%
- Conflict Resolution:
- 92%
- Strategy & Innovation:
- 90%
- Service Experience:
- 94%
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 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?
Pros
- Continually Improving Product
- Trustworthy
- Efficient Service
- Caring
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
Feature Ratings
Feature Engineering
Performance and Scalability
Algorithm Diversity
Data Labeling
Model Monitoring and Management
Model Tuning
Model Training
Data Exploration and Visualization
Ensembling
Data Pre-Processing
Openness and Flexibility
Vendor Capability Ratings
Quality of Features
Breadth of Features
Ease of Customization
Ease of IT Administration
Product Strategy and Rate of Improvement
Business Value Created
Availability and Quality of Training
Ease of Implementation
Usability and Intuitiveness
Ease of Data Integration
Vendor Support
TensorFlow TFX Reviews
- Role: Student Academic
- Industry: Education
- Involvement: End User of Application
Submitted Sep 2023
Wonderful very easy to use product
Likeliness to Recommend
Pros
- Helps Innovate
- Reliable
- Enables Productivity
- Trustworthy
Cons
- Vendor Friendly Policies
Mohd K.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Jul 2023
TFX: A Production-Ready Machine Learning Platform
Likeliness to Recommend
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
Muskan H.
- Role: Industry Specific Role
- Industry: Engineering
- Involvement: End User of Application
Submitted Jun 2023
Production-Ready Pipelines
Likeliness to Recommend
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
