Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of serverless computing
- Mastery over the use of the Serverless framework in conjunction with the Amazon Web Services (AWS) cloud
- The ability to dockerize a machine learning model
- Hands-on experience building an end-to-end machine learning pipeline in AWS
- Mastery over the use of AWS Lambda Functions and Step Functions in the context of data science
Learning Roadmap
1.
Basics of Serverless Computing
Basics of Serverless Computing
Step through AWS Lambda to manage single-purpose, stateless functions with serverless applications.
Course OverviewIntroductionServerless Implementation in AWS and the Serverless FrameworkHands-On: Deploy the Serverless Application and Inspect it in AWSMultiple Lambda Functions - Configuring Providers and PluginsMultiple Lambda Functions---Configuring Layers and DeploymentHands-On: Multiple Lambda Functions Using LayersQuiz: Serverless ComputingExercise: Deploy the Basic HTTP APISolution: Deploy the Basic HTTP API
2.
ML Model Dockers in AWS Lambda
ML Model Dockers in AWS Lambda
Solve challenges with deploying ML models using Docker in AWS Lambda efficiently.
3.
Serverless Architecture for Data Science
Serverless Architecture for Data Science
6 Lessons
6 Lessons
Examine serverless architectures for data science applications using AWS tools and orchestration.
5.
Appendix
Appendix
3 Lessons
3 Lessons
Solve problems in configuring AWS and Serverless environments for data science solutions.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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!
ABOUT THE AUTHOR
Konrad Semsch
Konrad is a predictive modelling practitioner passionate about ML, MLOps and deploying simple solutions to production that - just work!
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Evan Dunbar
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