• June 21, 2024

TensorFlow: End-to-End Open Source Machine Learning Platform.

TensorFlow is the art to create production-grade machine-learning models

 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.

Solutions to accelerate machine learning tasks


As an end-to-end machine learning platform TensorFlow helps you in the following:


    As an end-to-end machine learning platform

    TensorFlow helps you in the following:

  • Prepare data.
  • Build ML models.
  • Deploy models.
  • Implement MLOps.

Prepare Data

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. 

Build ML 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. 

Deploy Models

 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.   

Implement MLOps

 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.

What is TFX and how it works?

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.

  1. Machine Learning Metadata.
  2. TensorFlow Data Validation.
  3. TensorFlow Transformation.
  4. TensorFlow.
  5. TensorFlow Model Analysis.
  6. TensorFlow, TF Lite, and TFJS Service.

Models & Datasets

 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:

  1. TensorFlow Hub.
    --> Explore tfhub.dev
  2. Model Garden.
    --> Explore GitHub
  3. TensorFlow.js Models.
    --> Explore GitHub

Datasets:

  1. TensorFlow Official Datasets.
    --> Learn more
  2. Google research Datasets.
    --> Learn more
  3. Additional Dataset Resources.
    --> Dataset Search
    --> Google Cloud public datasets
    --> Kaggle datasets

TensorFlow Tools

 List of tools to support and accelerate TensorFlow workflows:

  1. Colab
    --> Learn more
  2. TensorBoard
    --> Learn more
    --> View Code
  3. What-If tool
    --> Learn more
    --> Get Started
  4. Ml Perf
    --> Learn more
  5. XLA
    --> Learn more
  6. TensorFlow Playground
    --> Learn more
  7. TPU research Cloud
    --> Learn more
  8. MLIR
    --> Learn more

Libraries & Extensions

 Here we list some Libraries and Extensions that will help you build advanced models and access domain-specific application packages that extend TensorFlow.

TensorFlow Certificate Program & TensorFlow Certification Network

 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.


TensorFlow Developer Certificate program overview


 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.

Learn ML

 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:

  1. For beginners
    basics of machine learning with TensorFlow
  2. For intermediate-level & experts
    Theoretical and advanced machine learning with TensorFlow
  3. For beginners
    TensorFlow for JavaScript development

Responsible AI

 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.


What is Responsible AI?


 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 in your ML workflow

Responsible AI practices can be incorporated at every step of the ML workflow based on the following

  • Define Problem
    Who is my Ml system for?
  • Construct and prepare data
    Am I using a representative dataset?
    Is there real-world/human bias in my data?
  • Build and train model
    What methods should I use to train my model?
  • Evaluate model
    How is my model performing?
  • Deploy and monitor
    Are there complex feedback loops?

TensorFlow Spark and Hadoop

 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:

  1. Startup
  2. Data Ingestion
  3. Shutdown

 TensorFlowOnSpark was developed by Yahoo and provides many important benefits listed below:

  • Easily migrate existing programs with less than 10 lines of code change.
  • Support all TensorFlow functionalities.
  • Server-to-server direct communication.
  • Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
  • Easily integrate with your existing Spark data processing pipelines.
  • Easily deployed on any platform like cloud, on-premise, and on CPUs or GPUs.

TensorFlow Documentation

 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

TensorFlow.org

Here I leave you a link to explore Google AI resources to guide your AI/ML journey!

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