During AWS re:INVENT 2017, AWS 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.
Check to Amazon SageMaker, Now it will redirect to SageMaker pannel & over there you have multiple options:-
- Ground Truth
- Notebook Instance Name:- Can choose anything.
- Notebook instance type: Here you have multiple options.
- 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:-
- Here we go, Successfully create our first Amazon Sagemaker Notebook Instance.