Training and Deploying an ML Model as a Microservice

prerequisites: Beginner HTML, Javascript, and Python; Basics of Docker, Basics of AWS, Basics of Lambda, Basics of NLP
skills learned: Use Machine Learning for NLP, Train and evaluate a sentiment analysis model, Deploy a Machine Learning model, Build a website to connect to model's API

Jon Peck

5 weeks · 5-10 hours per week · BEGINNER

In this liveProject, you’ll fill the shoes of a developer for an ecommerce company. Customers provide reviews of your company’s products, which are used to give a product rating. Until now, assigning a rating has been manual: contractors read each review, decide whether it’s positive or negative, and assign a score. Your boss has decided that this is too expensive and time consuming. Your mission is to automate this process, dramatically increasing the speed of rating calculations, 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 ratings, and build a small webpage using FaaS, containers, and microservices that can run your model for demonstration.

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 author

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.

Prerequisites

This liveProject will benefit both full-stack web developers and data scientists. If you’re a web developer, you’ll expand your skill set with valuable data science knowledge. If you’re a data scientist, you’ll develop techniques for deploying and demonstrating your models. To begin this liveProject, you will need to be familiar with the basics of Python. It would be helpful, but not essential, to know

TOOLS

Basics of Lambda or another FaaS
Basics of Docker
Basics of HTML & JavaScript
Basics of AWS or another IaaS provider

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.

Determine the tools and frameworks for your machine learning projects
Compare the capabilities of off-the-shelf solutions with those of a self-trained model
Train and evaluate your own Sentiment Analysis model in Python using NLTK
Host your model as a callable API endpoint
Write a simple HTML and JavaScript site to connect to your model’s API

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.

project outline

Introduction

Prerequisites Test

New module

Get Started

1. Existing Machine Learning Services

1.1. Existing Machine Learning Services

The Absolute Python Basics

Exploratory Data Analysis

1.2. Submit Your Work

2. Training Your Own ML Model

2.1. Training Your Own ML Model

Implementing Your Own Spam Filter

Text Mining and Text Analytics

2.2. Submit Your Work

3. Deploying as a FaaS

3.1. Deploying as a FaaS

Your First Lambda Function

3.2. Submit Your Work

4. Deploying as a Container Service

4.1. Deploying as a Container Service

Discovering Docker

Building Your Own Docker Images

4.2. Submit Your Work

5. Integrating Your Microservice

5.1. Integrating Your Microservice

Review of the XMLHttpRequest API

Debugging CORS Requests

5.2. Submit Your Work

Summary

Project Conclusions

FAQs

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