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.

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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

When an application is serverless, it means that developers can develop and run the application without the needing to manage the underlying servers.

When an application is serverless, it means there is no need for a server.

It means that developers can develop and run the application by managing the underlying servers.

What does it mean for an application to be serverless?

What does it mean for an application to be serverless?

The serverless framework can _ONLY_ deploy serverless services.

The serverless framework can ONLY deploy serverless services.

A `serverless.yml` file with basic sections defined and a corresponding `handler.py` file with the function definition.

What do you need to deploy a simple AWS Lambda function with the serverless framework?

What do you need to deploy a simple AWS Lambda function with the serverless framework?

What are the minimum configuration components in the `serverless.yml` file?

What are the minimum configuration components in the <code>serverless.yml</code> file?

The app is deployed to an S3 bucket and after that the AWS stack is deployed/updated based on the uploaded package.

Local files are packaged, converted into native CloudFormation language, and deployed to an S3 bucket. Finally, the AWS stack is deployed/updated based on the uploaded package.

The AWS account is created, the serverless framework is configured with AWS, and finally, the AWS stack is deployed/updated based on the Lambda function.

Which actions are performed by the serverless framework when deploying the application?

Which actions are performed by the serverless framework when deploying the application?

The serverless framework will not generate any additional resources beyond the ones specified in the `serverless.yml` file.

The serverless framework will not generate any additional resources beyond the ones specified in the <code>serverless.yml</code> file.

The serverless framework doesn’t allow developers to deploy multiple versions of the app from the same file to different AWS environments.

The serverless framework doesn’t allow developers to deploy multiple versions of the app from the same file to different AWS environments.

In order to deploy multiple Lambda functions/services, you need to have multiple `serverless.yml` files combined together.

In order to deploy multiple Lambda functions/services, you need to have multiple <code>serverless.yml</code> files combined together.

What is AWS Lambda’s maximum deployment package size?

What is AWS Lambda’s maximum deployment package size?

Allows us to share common dependencies across multiple Lambda functions with similar requirements.


Allows us to provide dependencies which exceed regular Lambda limits.

Allows us to exceed the limit of the size of the AWS distribution package.

**(Select all that apply.)** What are the reasons for using a Lambda layer?

(Select all that apply.) What are the reasons for using a Lambda layer?

By using the Lambda function in the `handler.py` file.

By using the `deploymentBucket` configuration in the `serverless.yml` file.

By using the `stepFunctions` configuration in the `serverless.yml` file.

How can you organize your AWS deployment artifacts in your AWS account?

How can you organize your AWS deployment artifacts in your AWS account?

What SLS syntax should be used in order to reference the `runtime` configuration from the `serverless.yml` provider section?

What SLS syntax should be used in order to reference the <code>runtime</code> configuration from the <code>serverless.yml</code> provider section?

Test your knowledge on the basics of serverless computing.

Basics of Serverless Computing

ML Model Dockers in AWS Lambda

Serverless Architecture for Data Science

Conclusion

Appendix

Quiz: Serverless Computing