TensorFlow is a free and open-source software library for machine learning.
It can be used across a range of tasks but has a particular focus on the training and inference of deep neural networks.
TensorFlow can run on multiple CPUs and GPUs. TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS.
- TensorFlow is an end-to-end open-source platform for machine learning.
- It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications.
- With TensorFlow, building and training ML models are easy and can be done using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
- Irrespective of the language we use, one can easily train and deploy models in the cloud, on-prem, in the browser, or on-device.
- TensorFlow models can also be run without a traditional computer platform in the Google Cloud Machine Learning Engine.
- TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.
- TS makes it easy to deploy new algorithms and experiments while keeping the same server architecture and APIs.
- It provides out-of-the-box integration with TensorFlow models but can be easily extended to serve other types of models.
- Servables are the central abstraction in TensorFlow Serving. Servables are the underlying objects that clients use to perform computation.
- TensorFlow Serving represents a model as one or more Servables. A machine-learned model may include one or more algorithms and lookup or embedding tables.
Life of a Servable:
- If you are looking for a better way of visualizing data with its graphical approach, then TensorFlow is the answer.
- TensorBoard provides the visualization and tooling needed for machine learning experimentation. It also allows easy debugging of nodes with the help of TensorBoard.
- TB enables tracking experiment metrics, visualizing models, profiling ML programs, visualizing hyperparameter tuning experiments, and much more.
- TensorBoard, TensorFlow’s visualization toolkit, is often used by researchers and engineers to visualize and understand their ML experiments.
2.Google Cloud Functions
- TensorFlow Enterprise includes Deep Learning VMs (GA) and Deep Learning Containers (Beta), which make it simple to get started and scale.
- TensorFlow Enterprise offers the same optimized experience and enterprise-grade features across Google Cloud managed services, like Kubernetes Engine and AI Platform.
- Whatever stage of development you are in, from development to deployment, Google Cloud offers an end-to-end workflow on TensorFlow.
- TensorFlow is an established framework for the training and inference of deep learning models.
- Google Cloud Functions offer a convenient, scalable, and economic way of running inference within Google Cloud infrastructure and allows you to run the most recent version of this framework.
- TensorFlow acts in multiple domains such as image recognition, voice detection, motion detection, time series, etc hence it suits the requirement of a user.
- TensorFlow Graphics aims at making useful graphics functions widely accessible to the community by providing a set of differentiable graphics layers and 3D viewer functionalities that can be used in your machine learning models of choice.
- TensorFlow Graphics comes with a TensorBoard plugin to interactively visualize 3d meshes and point clouds.
- Explicitly modeling geometric priors and constraints into neural networks opens the door to architectures that can be trained robustly, efficiently, and more importantly, in a self-supervised fashion.
4.Tools & Support
- TensorFlow offers multiple tools, and each tool has its own purpose.
- Tools such as CoLab, TensorBoard, ML Perf, TensorFlow Playground, MLIR used to accelerate TensorFlow workflows.
- TensorFlow is a community-driven project. TensorFlow community base is from all around the world.
- Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
- TensorFlow offers a vast library of functions for all kinds of tasks – Text, Images, Tabular, Video, etc. It also provides several add-on libraries and resources to deploy your production models anywhere.
- TensorFlow offers an easy and flexible model-building experience suitable for both experts and beginners.
- Integration of high-level libraries like Keras and Estimators makes it simple for a beginner to get started with neural network-based models.
- TensorFlow finds its use as a hardware acceleration library due to the parallelism of work models.
- Explore libraries to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow.