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Mastering Serverless Computing for Data Science

Name: Mastering Serverless Computing for Data Science
Price: 59 USD
Author: Konrad Semsch

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

Created by Konrad Semsch

Mastering Serverless Computing for Data Science

What's included

26 lessons across 5 modules
4 quizzes with instant feedback
in preconfigured environments
on every lesson

Join 3.1 million developers at

What you'll learn

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

26 Lessons4 Quizzes

Basics of Serverless Computing

Step through AWS Lambda to manage single-purpose, stateless functions with serverless applications.

Course Overview

Introduction

Serverless Implementation in AWS and the Serverless Framework

Hands-On: Deploy the Serverless Application and Inspect it in AWS

Multiple Lambda Functions - Configuring Providers and Plugins

Multiple Lambda Functions---Configuring Layers and Deployment

Hands-On: Multiple Lambda Functions Using Layers

Quiz: Serverless Computing

Exercise: Deploy the Basic HTTP API

Solution: Deploy the Basic HTTP API

ML Model Dockers in AWS Lambda

Solve challenges with deploying ML models using Docker in AWS Lambda efficiently.

ML as a Service (MLaaS)

Custom ML Dockers Images in AWS Lambda

Hands-On: Custom ML Docker Images in AWS Lambda

Quiz: ML Model in AWS Lambda

Exercise: Train and Evaluate ML Model

Solution: Train and Evaluate ML Model

Serverless Architecture for Data Science

6 Lessons

Examine serverless architectures for data science applications using AWS tools and orchestration.

Conclusion

Find out about serverless application development for machine learning model serving using AWS.

Final Words

Appendix

3 Lessons

Solve problems in configuring AWS and Serverless environments for data science solutions.

Scalable Machine Learning Model for Accurate Predictions on AWS

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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!

Learn more about Konrad

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