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Deploy ML models using AWS SageMaker

What is AWS Sagemaker?

AWS SageMaker enables developers to create, train, and deploy machine-learning models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

Basically we will be using MNIST dataset which contains 60000 handwritten single digits(0–9) & test set contains 10000 samples. We will use AWS SageMaker’s own k-means clustering algorithm to group numbers in 10 clusters.

First of all we need to go to AWS console -> S3 Bucket

Then we need to create a S3 Bucket.

Add Bucket name as (sagemaker-mclean2)

Add Region as (US-East) & Next.

Leave All Properties as Default.

Leave Manage Users as default.

Now Create new Bucket

Now in Amazon S3 tab New created bucket (sagemaker-mclean2) will be seen.

Now we need to open AWS SageMaker for Machine learning task.

Now Create New Notebook instance.

Add Notebook Instance name as (SagemakerDemo).

Add Notebook Instance type as (ml.t2.medium).

Now Create an IAM role. Add name of the S3 bucket (sagemakerdemo) & then Create role.

Now you can see new IAM Role is successfully created.

Add default VPC.

Add Subnet as US-East-2a.

Add default Security group.

Now in Notebook instances you can see new notebook (SageMakerDemo) created but it is in pending state.

After sometime status will be Inservice & you need to select Open.

Now jupyter notebook instance will be created.

Rename Notebook name (SageMakerDemo)

Basically we will be using MNIST dataset which contains 60000 handwritten single digits(0–9) & test set contains 10000 samples. We will use k-means clustering algorithm to group numbers in 10 clusters.

3. Plot 1 digit of the dataset using matplotlib