TensorFlow is an open-source machine learning framework developed by Google. It provides tools for building and deploying machine learning models, with capabilities ranging from deep learning to general numerical computations on dataflow graphs.
TensorFlow was developed by the Google Brain team, which is a deep-learning artificial intelligence research team at Google. The team was founded in 2011 by Andrew Ng, Jeff Dean, and Greg Corrado. The team built DistBelief, a proprietary machine learning system based on deep learning neural networks, which was used across diverse Alphabet companies in both research and commercial applications. TensorFlow was developed as a successor to DistBelief, and it was released to the public in 2015.
TensorFlow’s target customers are individuals and organizations that are interested in machine learning and artificial intelligence. TensorFlow can be used by researchers, developers, and businesses to build and deploy machine learning models. TensorFlow is used by a wide range of industries, including healthcare, finance, retail, and manufacturing.
TensorFlow has a large and growing user base, with over 1,500 repositories on GitHub mentioning TensorFlow, of which only 5 were from Google. TensorFlow’s customers include both small and large organizations, such as Airbnb, Coca-Cola, Intel, and Uber. The three top industries that use TensorFlow for Data Science And Machine Learning are Machine Learning (1,644), Artificial Intelligence (1,546), and Big Data (785).
Funding, Capital Raised, Estimated Revenue
TensorFlow is an open-source software library, and it is free to use. However, TensorFlow is also a part of Google’s cloud platform, which offers paid services for machine learning and artificial intelligence. TensorFlow’s estimated annual revenue is currently $2.1M per year, and TensorFlow’s estimated revenue per employee is $59,611.
Products and Services
TensorFlow offers a range of products and services for machine learning and artificial intelligence.
- TensorFlow Hub: a repository of pre-trained machine learning models for various tasks
- TensorFlow Lite: a lightweight version of TensorFlow designed for mobile and embedded devices
- TensorFlow Extended (TFX): a platform for building and deploying production machine learning pipelines
- Tools and libraries for data processing, visualization, and analysis
- A platform for building and deploying machine learning models, including deep neural networks.
TensorFlow’s main competitors in the machine learning and artificial intelligence space include the following:
Pros and Cons of TensorFlow
- Community Support: TensorFlow has a strong community, and a wide array of tutorials, videos, and pre-built models are available.
- Data Handling: Efficiently handles data using functionalities like TF Dataloaders.
- Versatility: Suitable for both beginners (with its Keras interface) and experts (with lower-level access).
- Scalability: TensorFlow can run on a single device or distributed systems with thousands of GPUs.
- Open Source: Being open-source, anyone can use and customize TensorFlow.
- Integration with Other Libraries: Provides support tools like Keras, TF Lite, and TFX.
- Visualization Tools: Offers tools like TensorBoard for visualization and debugging.
- Comprehensive Features: Includes many valuable features such as Tensorboard, Keras, integration with HDFS, and more.
- Pre-trained Models: TensorFlow Model Hub provides easy access to pre-trained models.
- Support for Multiple Devices: Allows for multicore and distributed training of models, optimizing for reduced training time.
- Flexibility: Allows for creating and training a wide range of machine learning models from simple to complex.
- Up-to-date Algorithms: Regular updates with the latest algorithms, especially those that can run on CNN models.
- Production-Ready: TensorFlow is mature and heavily used in production scenarios.
- Compatibility: Compatible with many programming languages like Java, Python, C++, Ruby, etc.
- Complexity for Beginners: TensorFlow can have a steep learning curve for newcomers, and its documentation can be confusing at times.
- Resource Intensiveness: TensorFlow is resource-intensive, requiring high computational power and a powerful GPU.
- Memory Usage: Uses a significant amount of memory, especially with certain deployments.
- Compatibility Issues: Some issues arise when using TensorFlow with Windows and with certain Python pip packages.
- Lack of Native Support: For certain popular packages like Decision Forests, there’s no native support.
- Version Conflicts: Regular updates can sometimes cause incompatibility with older applications, and there’s confusion between TensorFlow v1 and v2.
- Documentation: Some users found the documentation could be improved.
- Error Detection: Some users found it challenging to detect and resolve errors, especially on particular platforms.
- Heavy for Inferencing: TensorFlow can be heavy for inferencing, although TFLite helps for mobile.
- Limitations on GPUs: TensorFlow is primarily supported only on NVIDIA GPUs.
- Learning Curve: After crossing the beginner threshold, the learning curve steepens.
- Dependency Errors: At times, TensorFlow can throw errors due to dependency issues.
- Version Shifts: Big shifts between some versions can require additional work for compatibility.