Explore serverless computing with AWS Lambda, deploy ML pipelines, and manage applications efficiently without infrastructure worries.

Serverless 101 - Essential Patterns for Data Scientist.png

codes.tar.gz

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

In this last exercise, you'll combine all the skills you learned in this course to bring your self-built ML model to the next level. The goal of this exercise is to divide the functionality from the previous step and orchestrate it using AWS Step Functions. 


>Please note that every step of your state machine will have to read/write data to Amazon S3, as the maximum payload size that can be transferred between steps is limited to 256 KB. You can use your previously trained model object for this exercise. 
# Problem statement
The state machine that you will create should have three steps in total.

1. Given an input payload that indicates the location of raw train/test data in Amazon S3, the Lambda function should apply your data preprocessing function on each of those datasets respectively, and then persist it back to Amazon S3 under a different prefix, so that both dataset types can be distinguished. The output path for Amazon S3 should be passed as input to the next step.

2. Given the location of the previously processed datasets, this step should invoke the Lambda function , which will use the trained model to generate predictions for both the train and test datasets. Predictions should be persisted back to Amazon S3 and their path should be included in the payload of the state machine and passed to the next step.

3. In this final step, you need to write a Lambda function that will read the predictions from Amazon S3 and perform the model evaluation on the train/test data respectively. Depending on your ML problem, these can be any metrics of your choice. 

## Playground

Use the following code widget to perform your task:



Test the knowledge you've gained so far by applying it to this hands-on exercise.

Exercise: ML Model Using AWS Step Functions

Basics of Serverless Computing

ML Model Dockers in AWS Lambda

Serverless Architecture for Data Science

Conclusion

Appendix

Scalable Machine Learning Model for Accurate Predictions on AWS

Exercise: ML Model Using AWS Step Functions