Advantages of Using TensorFlow for Machine Learning Applications

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 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.

Why TensorFlow?

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 Architecture

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

TensorFlow Advantages

1. Data Visualization

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.

3. TensorFlow Graphics

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.

5. Powerful Library

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 make 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.

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