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.
89 Likeliness to Recommend
Down
3
Since last award
100 Plan to Renew
85 Satisfaction of Cost Relative to Value
Down
2
Since last award
Emotional Footprint Overview
- Product Experience:
- 90%
- Negotiation and Contract:
- 94%
- Conflict Resolution:
- 93%
- 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.
+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?
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
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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
Product Strategy and Rate of Improvement
Ease of IT Administration
Business Value Created
Availability and Quality of Training
Ease of Implementation
Usability and Intuitiveness
Ease of Data Integration
Vendor Support
TensorFlow TFX Reviews
Habeeblahi B.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Apr 2023
Easy to use, great features
Likeliness to Recommend
What differentiates TensorFlow TFX from other similar products?
TensorFlow TFX is different from other similar product due to its integration with TensorFlow which is an open-source machine learning framework and this makes it easy to leverage the vast ecosystem of TensorFlow tools, libraries, and pre-trained models.
What is your favorite aspect of this product?
My favorite aspect of TensorFlow TFX is the scalability which allows handling of large datasets and can scale to handle distributed training across multiple machines.
What do you dislike most about this product?
i have no feature i dislike in TensorFlow TFX.
What recommendations would you give to someone considering this product?
My main recommendation would be to take advantage of the TFX community. TensorFlow TFX has a large and growing community of users and developers. Take advantage of this community by joining forums, attending meetups, and asking questions.
Pros
- Helps Innovate
- Inspires Innovation
- Caring
- Altruistic
Kevin S.
- Role: Information Technology
- Industry: Technology
- Involvement: End User of Application
Submitted Feb 2023
Handy product for model creation of deep learning
Likeliness to Recommend
What differentiates TensorFlow TFX from other similar products?
It is having multiple libraries like Keras and other properties to create tensor where mathematical operations can be easily performed and is more scalable than other libraries.Also the deep learning neural networks are created using this tool for the tensorflow library which is easily accessible
What is your favorite aspect of this product?
Its user interface and command prompt
What do you dislike most about this product?
Everything is perfect for this product
What recommendations would you give to someone considering this product?
Highly recommendable for deep neural networks
Pros
- Helps Innovate
- Continually Improving Product
- Reliable
- Performance Enhancing
Ashay S.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Development, Integration, and Administration
Submitted Jan 2023
Great for Designing End to End ML pipelines
Likeliness to Recommend
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
