Top 5 Advantages of Tensorflow For Machine Learning Applications | Australia

5 Advantages 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 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 - open source machine learning platform

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 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.

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