Delve into serverless computing, develop AWS Lambda Functions, and deploy an end-to-end machine learning pipeline. Gain insights into efficiently managing ML applications without infrastructure concerns.

Serverless 101 - Essential Patterns for Data Scientist.png

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AWS_ACCESS_KEY_ID

AWS_SECRET_ACCESS_KEY

AWS_REGION

AWS_OUTPUT

AWS_BUCKET_NAME

SERVERLESS_ORG

SERVERLESS_APP

SERVERLESS_SERVICE

DATA_PATH

aws_serverless

aws_serverless2

aws_serverless3

aws_serverless4

aws_test

Challenge2-B

Challenge2-A

Challenge2-B-S

Challenge2-A-S

Challenge3-S

Challenge3

Challenge2t

Challenge2t-S

Serverless computing makes the life of a developer easy by eliminating the pains of managing servers. You only need to write code—the serverless cloud services, such as AWS Lambda Functions, take care of infrastructure on their own. The Serverless framework provides easy to use commands and dashboards to make it even easier to use cloud services.

In this course, you’ll learn what serverless computing is and how the Serverless framework works. You’ll then get hands-on experience developing and deploying an AWS Lambda Function. Next, you’ll learn how to provide additional libraries using Layers in AWS Lambda. Having covered the basics, you’ll then deploy an end-to-end machine learning pipeline to the AWS cloud with a serverless approach.

Mastery over the serverless framework will give you the ability to focus on your machine learning problem and pipeline, instead of worrying about infrastructure management. This will help you get your machine learning applications to market faster and scale them seamlessly!

Mastering Serverless Computing for Data Science

## Solution
The following steps explain how to create an HTTP API serverless application.

### Steps
* Enter the AWS credentials in the environment variables.

* Click "Connect." Following task will be performed when you click "Connect": 

  * AWS credentials are configured using the environment variables (`AWS_ACCESS_KEY_ID`,  `AWS_SECRET_ACCESS_KEY` , `AWS_REGION`, `AWS_OUTPUT`).
  * You are asked to log in to Serverless, which you can do either through the dashboard or console. Use the dashboard method. 
  * After logging in to Serverless, create a serverless application using the `serverless` command, then use the `AWS - Node.js - HTTP API`, enter the project (`app`) name, and select the appropriate `org`. 
  * Enter “yes” when asked to deploy the application. 


# Solution
The following steps explain how to create an HTTP API serverless application.

## Steps
* Enter the AWS credentials in the environment variables.

* Click "Connect." Following task will be performed when you click "Connect": 

  * AWS credentials are configured using the environment variables (`AWS_ACCESS_KEY_ID`,  `AWS_SECRET_ACCESS_KEY` , `AWS_REGION`, `AWS_OUTPUT`).
  * You are asked to log in to Serverless, which you can do either through the dashboard or console. Use the dashboard method. 
  * After logging in to Serverless, create a serverless application using the `serverless` command, then use the `AWS - Node.js - HTTP API`, enter the project (`app`) name, and select the appropriate `org`. 
  * Enter “yes” when asked to deploy the application. 


Let's take a look at the solution for this exercise.

Solution: Deploy the Basic HTTP API

Basics of Serverless Computing

ML Model Dockers in AWS Lambda

Serverless Architecture for Data Science

Conclusion

Appendix

Solution: Deploy the Basic HTTP API

Solution

Steps