Overview

## Become a Cutting-Edge Machine Learning Engineer Step into the exciting world of machine learning engineering with this comprehensive Track designed for aspiring professionals. You'll learn everything you need to know about model deployment, operations, monitoring, and maintenance to become a well-rounded machine learning engineer. ## Master the Fundamentals of MLOps Gain a deep understanding of the core concepts of MLOps as you: * Explore the modern MLOps framework and lifecycle * Learn to design, train, and deploy end-to-end models * Gain hands-on experience with key technologies like Python, Docker, and MLflow * Understand crucial concepts like CI/CD, deployment strategies, and concept drift ## Gain Practical Skills Through Real-World Projects Apply your knowledge to solve authentic challenges that mirror the day-to-day work of a machine learning engineer. You'll have the opportunity to develop predictive models for agriculture, forecast temperatures in London using advanced techniques, and build reliable data pipelines using ETL and ELT principles. ## Develop a Versatile Machine Learning Engineering Skill Set Throughout this Track, you'll gain expertise in building and deploying machine learning models in production environments, ensuring their performance remains optimal over time. You'll explore methods for monitoring models and addressing issues related to data and concept drift while leveraging data version control for efficient ML data management. Additionally, you'll learn how to implement CI/CD pipelines to streamline model development and deployment, making machine learning workflows more reliable and scalable. ## Prepare for a Junior Machine Learning Engineer Role Upon completing this Track, you'll have the knowledge and practical experience to confidently pursue junior machine learning engineer positions. You'll be equipped to: * Collaborate with data science teams to bring models from concept to production * Optimize model performance and ensure seamless integration with business systems * Continuously monitor and maintain deployed models to deliver reliable results * Contribute to the development of scalable and efficient machine learning infrastructure Note: This Track assumes prior knowledge of data manipulation, training, and evaluating machine learning models using Python. ## Unlock Your Potential in Machine Learning Engineering Start this transformative journey to become a sought-after machine learning engineer. With interactive courses, real-world projects, and expert instruction, you'll gain the skills and confidence to make a lasting impact in this cutting-edge field.

Syllabus

Supervised Learning with scikit-learn

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

MLOps Concepts

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

Introduction to Shell

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

Predictive Modeling for Agriculture
MLOps Deployment and Life Cycling

In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.

Introduction to MLflow

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

Predicting Temperature in London
ETL and ELT in Python

Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.

Introduction to Data Quality with Great Expectations

Ensure high data quality in data science and data engineering workflows with Python's Great Expectations library.

Introduction to Data Versioning with DVC

Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.

Monitoring Machine Learning Concepts

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Monitoring Machine Learning in Python

This course covers everything you need to know to build a basic machine learning monitoring system in Python

Introduction to Docker

Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.

CI/CD for Machine Learning

Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control

Taught by

Filip Schouwenaars, Folkert Stijnman, Nemanja Radojković, Tim Sangster, Hakim Elakhrass, Weston Bassler, Jake Roach, Joshua Stapleton, Ravi Bhadauria, and Maciej Balawejder