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Amazon SageMaker : Build & Deploy ML Model Easily

During AWS re:INVENT 2017AWS releases AmazonSageMaker to make it easier to build and deploy machine learning models

Cloud services are designed to take away a lot of the complexity associated with managing a particular process, whether that’s software or infrastructure. Today, machine learning is quickly gaining traction with developers, and AWS wants to help remove some of the obstacles associated with building and deploying machine learning models.

Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train and Deploy to production machine learning models at scale .

How to begin with Amazon sagemaker? 

To begin with, Amazon SagaeMaker service you first need to create your AWS account. When you sign up for Amazon Web Services (AWS), your AWS account is automatically signed up for all services in AWS, including Amazon SageMaker. You are charged only for the services that you use.

After signup hop over to services and search for SageMaker. You will see something like -:Sagemaker

Check to Amazon SageMaker, Now it will redirect to SageMaker pannel & over there you have multiple options:-

  • Ground Truth
  • Notebook
  • Training
  • Inference

sagemake notebookMove to Notebook instance & open it, Here you are ready to create your first SageMaker notebook. Hop over to  ” Create notebook instance “. 

Now you need to choose out your preference and fill them according to your uses.SageMaker Instance

  • Notebook Instance Name:- Can choose anything.
  • Notebook instance type: Here you have multiple options.Instance type
  • Make sure you select instance according to your need. Because here you need to pay according to the type of instance you choose.
  • IAM Role: Notebook instances require permissions to call other services including SageMaker and S3 bucket. S3 Bucket store data.  How to create an S3 bucket.
  • While you are creating S3 bucket make sure that that your notebook and bucket both lie in the same region.
  • Here the final step to launch your  AWS SageMaker notebook. click on start wait 5 min:-Launch Notebook instance
  • Here we go, Successfully create our first Amazon Sagemaker Notebook Instance.

About the author

Vikram singh

Founder of Ai Venture,
An artificial intelligence specialist who teaches developers how to get results with modern AI methods via hands-on tutorials.
GANs are my favorite one.

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