10+ TensorFlow Alternatives and Competitors

TensorFlow

Rating

4.5

Pricing

Custom

Reviews

3400+ Reviews

Category

Artificial Intelligence Software

Industry

SaaS

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TensorFlow is an open-source library for machine learning. It makes the development and training of machine learning easier. It contains an extensive versatile collection of libraries, tools, and resources to help developers and researchers deploy ML models with fewer complexities. It contributes to convenient model creation, dynamic research experimentation, and increased ML production.

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Top Alternatives for TensorFlow

  1. #1Manthan Artificial Intelligence
  2. #2Rainbird
  3. #3Infosys NIA
  4. #4Wipro HOLMES
  5. #5Amazon SageMaker
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No. Logo Name Website Rating Pricing Reviews
1 Manthan Artificial Intelligence Visit Now 4.3 Custom 3400+ Reviews
2 Rainbird Visit Now 4.4 Custom 2300+ Reviews
3 Infosys NIA Visit Now 4.6 Custom 2800+ Reviews
4 Wipro HOLMES Visit Now 4.4 Custom 4500+ Reviews
5 Amazon SageMaker Visit Now 4.5 $0.10 3000+ Reviews
6 CVViZ Visit Now 4.5 $29 3450+ Reviews
7 Salesforce Einstein Visit Now 4.6 $25 3790+ Reviews
8 Microsoft Azure AI Visit Now 4.5 $ 931 3500+ Reviews
9 Google AI Visit Now 4.7 Custom 1654+ Reviews
10 IBM Watson Visit Now 4.8 Custom 1757+ Reviews

Features

  • Access Control
  • Analytics
  • API
  • Accounting Integration
  • Agile Software Development
  • Application Integration
  • Assessment Management
  • Assignment Management
  • Automated Billing
  • Business Intelligence
  • CRM Integration
  • Campaign Analysis
  • Collaboration Tools
  • Collaborative Workspace
  • Team Collaboration

Q & A

What’s the pricing method for TensorFlow?

There’s none. It’s open-source and is, therefore, free to use and download. There’s no charge needed for commercial use as well. However, TensorFlow encourages external researchers and developers to contribute code and improvements. Their site indicates a detailed instruction on how to make one.

Does TensorFlow only run on Python?

No, it doesn’t only run Python. It also accommodates other languages such as JavaScript, C++, TypeScript, HTML, and Jupyter Notebook.

What is TensorFlow Lite?

TensorFlow Lite allows the conversion of TensorFlow tools to work in embedded, mobile, and IoT devices.

Is every TensorFlow operation available in TensorFlow Lite?

Unfortunately, no. TensorFlow Lite can’t have all the operations of the standard program to keep it agile in compact systems. TensorFlow’s website offers a comprehensive compatibility guide. This helps users get access to an updated list of activities that the lite version supports. Be sure to visit their site for more detailed information.

Reviews

“ Awesome ”
4.5/5 (overall) - Izuchukwu U
Pros:
  • TensorFlow is fairly easy to use, with adequate tutorials to get any user started quickly.
  • Tooling around TensorFlow, such as TensorBoard, is a gold standard: it has made the training and debugging process so much easier compared to most other deep learning platforms.
Cons:
  • Prior to TensorFlow 2.0, setting up data ingestion for TensorFlow can be a huge pain. So much so that TensorFlow Lite and alternatives such as Keras make it more palatable. Things are changing with TensorFlow 2.0 though.
  • Some error messages from TensorFlow can be quite difficult to understand. For instance, a recent error using the dot product layer in TensorFlow 2.0 made it seem like there was a problem with data ingestion, but by downgrading to TensorFlow 1.14.0, the problem disappears.
“ It is easy to use ” ”
4.4/5 (overall) - Aanu B
Pros:
  • Support for many libraries and programming languages.
  • Ability to use GPU and TPU – hence faster execution.
Cons:
  • Graphic interface to create layers can help beginners.
  • Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don’t focus on that.
“ Learning Curve ”
4.3/5 (overall) - Andrew C
Pros:
  • Low effort in getting started in development, hence ease of learning.
  • Detailed and more functional implementation of various algorithms.
Cons:
  • Better support to integrate with files on the cloud.
  • Performance issues on a low scale system.

Alternatives