Machine Learning DevOps Engineer

An Introduction to Machine Learning DevOps Engineer

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Introduction

Get introduced to clean code principles, why and when to use them, and the history of clean code. Then, see what you'll be able to build by the end of the course!

Coding Best Practices

Learn coding best practices, such as clean and modular code, code efficiency, refactoring, documentation, and linting.

Working with Others Using Version Control

Version control is crucial for any coding project, but becomes even more important when working in teams. Another new area in working with teams is the code review, which you'll also learn about here.

Production Ready Code

Find more coding best practices here, such as handling errors, testing and logging, as well as addressing model drift in machine learning models.

Predict Customer Churn with Clean Code

Take a colleague's messy juypter notebook for building a customer churn prediction model and implement all of the clean code principles you have learned throughout the course!

Introduction to Reproducible Model Workflows

Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.

Machine Learning Pipelines

Build out machine learning pipelines, as well as learning how to version data and model artifacts.

Data Exploration and Preparation

Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.

Data Validation

Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.

Training, Validation and Experiment Tracking

Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.

Final Pipeline, Release and Deploy

Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.

Build an ML Pipeline for Short-term Rental Prices in NYC

Create a re-usable end-to-end pipeline for predicting short-term rental prices in New York City!

Introduction to Deploying a Scalable ML Pipeline in Production

We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.

Performance Testing and Preparing a Model for Production

In this lesson, we will cover performance testing and preparing a model for production.

[Optional] Data and Model Versioning

In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.

CI/CD

We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.

API Deployment with FastAPI

Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live.

Deploying a ML Model to Cloud Application Platform with FastAPI

Welcome to ML Model Scoring and Monitoring

This lesson will talk about goals for the course, when to use ML model scoring and monitoring, stakeholders, the history of the field, and the tools and dependencies you need to be aware of.

Model Training and Deployment

This lesson will talk about model training and deployment. We’ll focus on automating the training and deployment process and making sure that the trained, deployed models are ready to be monitored.

Model Scoring and Model Drift

This lesson will discuss model scoring and model drift, an important part of the continuous monitoring that makes sure your deployed model remains as accurate as possible.

Diagnosing and Fixing Operational Problems

There are problems that can come up in deployed projects. So this lesson will talk about diagnosing and fixing operational problems, a crucial part of the post-deployment machine learning process.

Model Reporting and Monitoring with API's

This lesson will discuss model reporting and monitoring with APIs which can be used as an automatic interface with your ML project.

Project: A Dynamic Risk Assessment System

The final project for this course will be a dynamic risk assessment system in which you will build and monitor an ML model to predict attrition risk.

Congratulations!

Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.

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