Training and Deploying an ML Model as a Microservice you own this product

prerequisites
basic Python • basic machine learning • basic AWS • basic Python package managers
skills learned
use an existent sentiment classification API • create a web API to expose an ML model • build a simple demo website
Jonathan Peck and Alex Gascon
5 weeks · 5-10 hours per week · BEGINNER

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liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $37.49 $49.99 you save $12 (25%)
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In this liveProject, you’ll fill the shoes of a developer for an e-commerce company looking to build a machine learning solution to help identify bad product reviews. If one of your company’s products receives too many bad reviews, it’s policy to take it down from the e-commerce store. Until now this process has been manual—but your boss has decided that this is too expensive and time-consuming.


Your mission is to automate this process, dramatically increasing the speed of identifying bad reviews, and decreasing the cost to your company. To complete this project you will have to train a machine learning model to recognize and rank positive and negative reviews, expose this model to an API so your website and partner sites can benefit from automatic rankings, and build a small webpage that can run your model for demonstration.


Updated March 2022

  • Revised solutions for each milestone
  • Added click-to-reveal help boxes with hints and guidance
  • Added recommended books and learning resources
  • Edited to focus on Amazon Web Services
  • Added material on using AWS Lambda to support machine learning
  • Clarified and simplified project prerequisites
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project authors

Jonathan Peck
Jon Peck is a full-stack developer and educator with a focus on DevOps and machine learning. Jon currently works as a technology advocate for GitHub. His prior positions include Algorithmia (a Seattle-based MLOps company backed by Google's AI fund), Massachusetts General Hospital, Cornell University, a bevy of small startups, and independent consulting. He constantly strives to make technical concepts digestible, demonstrating the value of new technology at every level, from developers through execs, and has been honored to speak at AI Next, API World, DeveloperWeek, O'Reilly AI, ODSC, and OSCON.
Alex Gascon
Alex Gascon is a software engineer passionate about learning, productivity, and psychology. He thinks of software development as something composed of more than just the programming language you use and focuses on concepts like good practices, scalability, and reliability, or productivity tools or techniques. He currently works as a Backend Engineer at Stripe, and his past experience includes AWS and startups like CoverWallet or MeetYourTalent.

prerequisites

This liveProject will benefit both full-stack web developers and data scientists. If you’re a web developer, you’ll expand your skillset with valuable data science knowledge. If you’re a data scientist, you’ll develop techniques for deploying and demonstrating your models.


TOOLS
  • Basics of Python
  • Basics of Lambda or other function-as-a-service (FaaS)
  • Basics of HTML and JavaScript
  • Basic Python package manager
  • Basics of AWS
TECHNIQUES
  • Basics of NLP

you will learn

In this liveProject, you’ll learn the diverse skill set necessary to design, create, host, and give a live demonstration of a machine learning model. You’ll learn how all parts of machine learning tie together, and how to effectively deploy a model to production.


  • Use an existing sentiment classification API (Amazon Comprehend)
  • Create and train your own classification model, and test its performance
  • Create a web API to expose your model through HTTP
  • Deploy your model to the cloud, either as a serverless function (Amazon Lambda) or as a Docker container, to make it accessible from the internet
  • Build a minimal website that uses your deployed model, to ensure that it works as expected and show the results to non-technical people

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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