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Table of contents
1.
Introduction 
2.
How to get started?
2.1.
Create an AWS Account
2.2.
Create an IAM Administrator User and Group
3.
Machine Learning with the SageMaker Python SDK
3.1.
Steps to make a Machine Learning model on AWS SageMaker
4.
Frequently Asked Questions
4.1.
What are PyTorch and TensorFlow?
4.2.
Is TensorFlow a framework or library?
4.3.
Do I need to know Python for TensorFlow?
4.4.
Is TensorFlow an API?
5.
Conclusion
Last Updated: Mar 27, 2024

TensorFlow on AWS

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Introduction 

TensorFlow is one of many deep learning frameworks accessible to researchers and developers looking to incorporate machine learning into their applications. TensorFlow is supported widely by AWS, allowing users to build and deploy models in computer vision, natural language processing, speech translation, and more.

Amazon SageMaker, a fully managed machine learning service that makes building, training, and deploying TensorFlow models at scale straightforward and cost-effective, is a good place to start with TensorFlow on AWS. If you prefer to manage your infrastructure, you can utilise the AWS Deep Learning AMIs or AWS Deep Learning Containers, which are designed from the ground up and optimised for performance with the newest version of TensorFlow.

How to get started?

Create an AWS Account

You create an AWS account in this part. Skip this step if you already have an AWS account.

Your AWS account is immediately signed up for all AWS services, including SageMaker, when you sign up for Amazon Web Services (AWS). You are only charged for the services you utilise.

To create an AWS account

  1. Go to https://portal.aws.amazon.com/billing/signup and fill out the form.
  2. Follow the instructions on the website.
  3. Receiving a phone call and entering a verification code on the phone keypad are required steps in the sign-up process.

Please write down your AWS account ID because you'll need it for the next task.

Create an IAM Administrator User and Group

When you create an AWS account, you get a single sign-on identity with access to all of the account's AWS services and resources. The AWS account root user is the name for this identity. You may access all of the AWS resources in your account by logging in to the AWS interface with the email address and password you used to create the account.

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Machine Learning with the SageMaker Python SDK

Use the SageMaker Python SDK to train, validate, deploy, and evaluate an ML model in a SageMaker notebook instance. AWS SDK for Python (Boto3) and SageMaker API activities are abstracted by the SageMaker Python SDK. It lets you connect to and orchestrate other AWS services like Amazon Simple Storage Service (Amazon S3) for storing data and model artefacts, and Amazon Elastic Container Registry (ECR) for importing and servicing machine learning models, and Amazon Elastic Compute Cloud (Amazon EC2) for training and inference.

You may also use SageMaker features to assist you with every stage of the ML cycle, including data labelling, data preprocessing, model training, model deployment, prediction performance evaluation, and model quality monitoring in production.

Steps to make a Machine Learning model on AWS SageMaker

  • Step 1: Create an Amazon SageMaker Notebook Instance
  • Step 2: Create a Jupyter Notebook
  • Step 3: Download, Explore, and Transform a Dataset
  • Step 4: Train a Model
  • Step 5: Deploy the Model to Amazon EC2
  • Step 6: Evaluate the Model
  • Step 7: Clean Up

More information about all these steps can be found on AWS documentation, and a lot more to read there. 

Frequently Asked Questions

What are PyTorch and TensorFlow?

TensorFlow is developed by Google Brain and is actively used at Google both for research and production needs. Its closed-source predecessor is called disbelief. PyTorch is a cousin of the Lua-based Torch framework, which was developed and used at Facebook.

Is TensorFlow a framework or library?

TensorFlow is Google's open-source AI framework for machine learning and high-performance numerical computation. TensorFlow is a Python library that invokes C++ to construct and execute dataflow graphs. It supports many classification and regression algorithms and, more generally, deep learning and neural networks

Do I need to know Python for TensorFlow?

There are many different ways to use TensorFlow.

For one, it supports lots of languages. The most commonly-used one is probably Python, followed by JavaScript. Additionally, there is support for Swift, C, Go, Java, Haskell, C#, Go, and more.

Is TensorFlow an API?

TensorFlow includes APIs for both generating and executing TensorFlow graphs in multiple languages. Although the Python API is currently the most full and easiest to use, other language APIs may be easier to incorporate into projects and may provide some performance benefits in graph execution.

Conclusion

So, in a nutshell, there are a lot of things which can be explored under the hood of AWS, mostly for all ML applications Sagemaker is used in AWS. 

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