Serverless 101 - Essential Patterns for Data Scientist (Book + code)
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Serverless 101 - Essential Patterns for Data Scientist

Hands-on guideline on deploying applications using for data scientists

About the Book

This 4-chapter book covers essential patterns of deploying ML applications using the framework. It covers common pitfalls and offers ready-to-use examples you can immediately apply to your own use cases. Throughout the book we will slowly build up the intuition behind different functionalities that the framework offers and how to tweak it. This book is mostly recommended to users who know that is a tool they would like to use, but lack the details how to efficiently put everything together. I hope you will enjoy these ready-to-use recipes!

The book will cover the following areas and concepts:

  • Initializing the project, first deployment and the dashboard
  • Basic serverless yaml components
  • File syntax, references and special helper functions
  • Handling per-environment configuration through params and config files
  • Isolating function dependencies in multi-function deployments
  • Deploying large dependencies through Lambda layers
  • Working with custom Docker images
  • Deploying a ML model to AWS Lambda
  • Integration with services such as EventBridge and StepFunctions
  • Helpful serverless plugins

The bundle consists of a zipped file with the entire codebase with extensive notes and further references so that the readers could further deepen their knowledge. Bear in mind that the book already assumes that the user is already somewhat familiar with IaC tools and working in the cloud, and such details will not be extensively explained. It is also not a typical book, but rather a Power Point presentation converted to a PDF file, but don't worry - it will have all the knowledge you need!

Future versions of this book are planned and I've started working on them. They will mainly cover the following topics:

  • expanding on examples and integrations using Step Functions
  • using to deploy and orchestrate AWS Batch jobs for ML model training and batch scoring
  • integration os with databases for ML use-cases
  • Share this book

  • Categories

    • Amazon Web Services
    • AWS
    • Docker
    • Infrastructure as Code
    • Machine Learning
    • Python
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About the Author

Konrad Semsch
Konrad Semsch

Konrad is a predictive modelling practitioner passionate about ML, MLOps and deploying simple solutions to production that - just work! He's worked several years in the area of data science and machine learning, deploying a wide variety of solutions at scale, having worked both at small startups, as well as large enterprise. Born and raised in Poland, Konrad currently lives with his wife in Essen, Germany. In his free (apart from writing this book...) he enjoys bouldering, volleyball and all kinds of watersports.

Table of Contents

  • Getting started - bare minimum example
  • syntax deep-dive and dependencies management
  • Serving an ML model in a custom Docker image
  • Integrating Lambdas with other services such as Event Bridge and StepFunctions
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