This course covers the tools and techniques commonly utilized for production machine learning in industry. Students learn how to provide web interfaces for training machine learning or deep learning models with Flask and Docker. Students will deploy models in the cloud through Amazon Web Services (AWS), gather and process data from the web, and display information for consumption in advanced web applications using Plotly and D3.js. Students use PySpark to make querying even the largest data stores manageable.
In this course you will acquire a set of key skills connecting data science and data modeling with the back end and front end web tools allow you to deploy models to the web. Mastering these skills will make you a more versatile Data Scientist or Data Engineer.
Course Delivery: Online | 7 weeks | 13 sessions
Course Credits: 3 units | 37.5 Seat Hours | 75 Total Hours
Students by the end of the course will be able to:
- Implement Advanced Visualizations using a Chart.js/D3.js Frontend, and a Python Backend
- Implement a Machine Learning or Deep Learning model on a Web App using Flask and Flask-RESTPlus
- Understand containers and be familiar with the Docker ecosystem
- Dockerize a Flask Web App containing a Machine Learning or Deep Learning Model and deploy it on Heroku, and also on AWS (Amazon Web Services)
- Understand the PySpark ecosystem and work on Big Data using PySpark, H2O and Pandas
Course Dates: Thursday, January 21 – Thursday, March 4, 2021 (7 weeks)
Class Times: Tuesday, Thursday at 2:45 pm–5:30 pm (13 class sessions)
| Class | Date | Topics |
|---|---|---|
| - | Tue, Jan 19 | No Class |
| 1 | Thu, Jan 21 | Introduction and accessing O'Reilly books through the MARINet library service |
| 2 | Tue, Jan 26 | Full Stack Deep Learning setup for labs |
| 3 | Thu Jan 28 | Lab 1: Building an Interactive app with Chartist |
| 4 | Tue, Feb 2 | Lab 2: Model Deployment Using Flask: Digit Recognizer Web App |
| 5 | Thu, Feb 4 | Lab 3: Nasdaq Stock Prices Visualization with D3 |
| 6 | Tue, Feb 9 | Lab 4: Deploying an ML model to the Web on Heroku, Part 1 |
| 7 | Thu, Feb 11 | Lab 5: Deploying an ML model to the Web on Heroku, Part 2 |
| 8 | Tue, Feb 16 | Lab 6: INtroduction to Docker, Part 1 |
| 9 | Thu, Feb 18 | Lab 7: Introduction to Docker, Part 2 |
| 10 | Tue, Feb 23 | Lab 8: Apache Spark, Part 1 |
| 11 | Thu, Feb 25 | Lab 9: Apache Spark, Part 2 |
| 12 | Tues, Mar 2 | Lab 10: Apache Spark, Part 3 OR SQL, Part 1 |
| 13 | Thu, Mar 4 | Lab 11: Apache Spark, Part 4 OR SQL, Part 2 |
Class Assignments are on GradeScope
- HW1: Chartist Flask App
- HW2: MNIST Digit Recognizer Flask App
- HW3: Dockerize Machine Learning Model and Deploy on AWS
- HW4: Apache Spark #1
- HW5: Apache Spark #2 OR SQL
- Extra Credit: Nasdaq Stock Prices Visualization App
If you have a disability that needs an accommodation such as extended time or a different format, please take advantage of our accommodations program, by filling out the intake form.
To pass this course you must meet the following requirements:
- Complete 4 of the 5 homework assignments with a grade of 70% or higher
Additional resources you may need (online books, etc.) can be found in the library linked below:
- Program Learning Outcomes - What you will achieve after finishing Make School, all courses are designed around these outcomes.
- Grading System - How grading is done at Make School
- Code of Conduct, Equity, and Inclusion - Learn about Diversity and Inclusion at Make School
- Academic Honesty - Our policies around plagerism, cheating, and other forms of academic misconduct
- Attendance Policy - What we expect from you in terms of attendance for all classes at Make School
- Course Credit Policy - Our policy for how you obtain credit for your courses
- Disability Services (Academic Accommodations) - Services and accommodations we provide for students
- Online Learning Tutorial - How to succeed in online learning at Make School
- Student Handbook - Guidelines, policies, and resources for all Make School students