TensorFlow was initially released in 2015 under the Apache License as a free open-source software library. then Google released version 2.0 of TensorFlow in 2019 considering it as Google Brain's Second generation System!
So whether you are an expert or a starting beginner, TnesorFlowmakes it easy for you to build ML models and deploy them using their end-to-end platform. An entire ecosystem to help you solve problems with machine learning.
As an end-to-end machine learning platform
TensorFlow helps you in the following:
As long as Data is the most important factor of your ML endeavors. TensorFlow offers amazing tools to help you in cleaning and process data on an advanced high scale.
Their most common tools consist of Standard Datasets for start and initial training accompanied by accurate validation. In addition to highly scalable Data Pipelines for loading data.
When it comes to input transformation Tensorflow rushes in offering their Processing Layers tools. Besides those mentioned tools come other important tools to Validate and Transform large and huge datasets.
All this without mentioning the Responsible AI that helps in uncovering and eliminating bias in order to establish well fair, clean, and ethical outcomes from your models.
TensorFlow offers users the opportunity to build and fine-tune their models using the EcoSystem which is built on the Core Framework that helps streamline model construction, training, and export.
TensorFlow contains a huge variety of tools that help you track the development of your project in addition to the improvement of your model's lifecycle; some of these common tools are Model Analysis, TensorBoard, and the famous Keras that makes your life easy through model iteration and easy debugging.
Whether on the device, on-premise, in the browser, or in the cloud TensorFlow nourish its users with amazing robust capabilities so they can deploy its models on any environment like servers, edge devices, microcontrollers, CPUs, GPUs, and much more.
What makes their Serving special is that they run and are compatible with the most advanced processors in the technology world, especially Google's custom Tensor Processing Units (TPUS).
In addition to all these amazing characteristics, the TensorFlow Lite framework is perfect for mobile and edge computing devices, and the Tensorflow.js framework allows you to run with just a web browser.
In order to implement the best practices for data automation, performance monitoring, and model tracking and training, TensorFlow offers accurate production-level tools to obtain critical success.
TFX provides software frameworks and tooling for MLOps deployments, detecting any error or issue as your data and models evolve.
TFX is an end-to-end platform for deploying production ML pipelines after finishing model research.
A TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually.
Models and Datasets created by the community TensorFlow are available in repositories and other sources that only need to be explored.
Here is a list of resource links.
MODELS:
Datasets:
List of tools to support and accelerate TensorFlow workflows:
Here we list some Libraries and Extensions that will help you build advanced models and access domain-specific application packages that extend TensorFlow.
Demonstrate your proficiency in using TensorFlow to solve deep learning and Ml problems. Get recognized and differentiate yourself with the TensorFlow Developer Certificate and join their Certificate Network.
The Exam Cost $100 USD
The goal of this certificate is to provide anyone the opportunity to showcase their expertise in ML. The certificate is intended as a foundational certificate for students, developers, and data scientists who are willing to demonstrate their machine-learning skills using TensorFlow.
The assessment exam is developed by the TensorFlow team. All those who pass the seams can join the TensorFlow Certificate network and Display And share their Badges on pages and social media so they can share their level of TensorFlow expertise.
Master Your Path
Becoming an expert in machine learning needs a strong foundation in four learning areas:
Coding skills, Math and stats, ML Theory, Build your own projects.
TensorFlow offers a huge resource library to choose your own learning path, or you can start with TensorFlow's curated curriculums.
List of the 3 curriculums:
TensorFlow is committed to helping programmers learn how to integrate Responsible AI practices into their ML workflow by sharing a collection of resources and tools with the ML community.
The development of AI is creating new opportunities to solve challenging, real-world problems. It is based on Recommending best practices for AI, Fairness, Interpretability, Privacy, and Security.
Responsible AI practices can be incorporated at every step of the ML workflow based on the following
TensorFlowOnSpark brings to apache scalable deep learning on the clusters Spark and Hadoop
To Be able to enable distributed deep learning on a cluster of GPU and CPU servers, TensorFlowOnSpark combined multiple features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, with the goal to minimize the number of code changes required to run existing models and programs of TensorFlow on a shared grid.
The created API help users manage the TensorFlow cluster in 3 simple steps:
TensorFlowOnSpark was developed by Yahoo and provides many important benefits listed below:
To have a full idea and clear info about TensorFlow, General Overview, Versions, Community supported languages, resources, and much more. Please visit their official website
