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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Real user data aggregated to summarize the product performance and customer experience.
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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
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?
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Enables Productivity
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
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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
TensorFlow TFX Reviews
Vijaya Phanindra K.
- Role: Information Technology
- Industry: Electronics
- Involvement: End User of Application
Submitted Aug 2024
Use Tensorflow in ML and DL applications
Likeliness to Recommend
What differentiates TensorFlow TFX from other similar products?
Tensorflow provides vast libraries along with Python which is more reliable for coding
What is your favorite aspect of this product?
Keras and tensorflow libraries integration with python
What do you dislike most about this product?
Sometimes I felt the libraries gets updated too frequently which make me to change my code more often
What recommendations would you give to someone considering this product?
Tensorflow can be used in Linux environment as well with which we can integrate hardware like Raspberry pi
Pros
- Helps Innovate
- Respectful
- Altruistic
- Acts with Integrity
Kumar A.
- Role: Information Technology
- Industry: Engineering
- Involvement: End User of Application
Submitted Mar 2024
Fabulous And User friendly
Likeliness to Recommend
What differentiates TensorFlow TFX from other similar products?
TensorFlow Extended (TFX) distinguishes itself by providing an end-to-end platform specifically tailored for productionizing machine learning models at scale, offering features like built-in support for TensorFlow, standardized components for data ingestion, validation, training, evaluation, and deployment, along with integration with TensorFlow Serving and Apache Beam for distributed processing, ensuring robustness, scalability, and ease of deployment in production environments.
What is your favorite aspect of this product?
TFX's comprehensive suite of tools and standardized components streamline the end-to-end process of deploying machine learning models at scale, ensuring efficiency and reliability in production environments.
What do you dislike most about this product?
I have no dis;like
What recommendations would you give to someone considering this product?
I would recommend thoroughly evaluating your organization's specific requirements and existing infrastructure to ensure compatibility and alignment with TFX's capabilities. Additionally, consider investing time in understanding TFX's architecture and components to maximize its benefits and streamline integration into your machine learning workflows.
Pros
- Reliable
- Enables Productivity
- Trustworthy
- Unique Features
Niharika M.
- Role: Information Technology
- Industry: Engineering
- Involvement: IT Leader or Manager
Submitted Mar 2024
Easy to deploy large model
Likeliness to Recommend
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
