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.
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
- Basics of Lambda or another FaaS
- Basics of Docker
- Basics of AWS or another IaaS provider
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