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

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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

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. 

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