Building, Evaluating, and Operationalizing ML Models

Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive into the entire process of building, evaluating, and operationalizing machine learning (ML) models. Starting with data exploration, you'll learn how to select the right algorithms for regression and classification tasks and fine-tune them for optimal performance. As you progress, you'll gain hands-on experience with tools like Azure ML Studio, experimenting with model customization, feature engineering, and advanced algorithms such as XGBoost and Neural Networks. You'll also discover how to evaluate models, optimize performance, and deploy them effectively. The course offers practical demos, empowering you to implement everything from simple models to complex pipelines in ML workflows. Throughout the course, you'll explore model evaluation techniques, including cross-validation and performance metrics, and learn how to address issues like overfitting and model drift. You'll also engage with ML-Ops concepts, discovering how to structure scalable pipelines, automate workflows, and manage the lifecycle of your models. This course is perfect for those looking to gain real-world ML skills, especially those interested in Azure ML Studio and automated pipelines. Whether you’re starting with basic ML concepts or expanding your knowledge, this course provides a comprehensive guide. You’ll learn to make data-driven decisions while optimizing the end-to-end machine learning lifecycle. By the end, you'll be prepared to build and deploy models that are both efficient and scalable, while also staying on top of versioning and performance monitoring.

Syllabus

Building Machine Learning Models

In this module, we will guide you through the process of building machine learning models using Azure ML Studio. You will explore various algorithms, including ensemble methods and advanced models, while learning how to select and fine-tune the best algorithm for your dataset. With practical demos, you’ll gain hands-on experience building, customizing, and optimizing models to meet your specific needs.

Model Evaluation and Optimization

In this module, we will dive into the evaluation and optimization of machine learning models. You will learn how to assess model performance using key metrics and cross-validation techniques, and explore methods for improving model efficiency. Additionally, you will understand the importance of tracking model drift and how to retrain models to keep them relevant and effective over time.

Machine Learning Pipelines (ML-OPS)

In this module, we will explore the power of machine learning pipelines for automating and scaling ML workflows. You will learn to design, build, and manage reusable pipelines, and incorporate custom code to tailor your processes. By the end of this section, you'll be equipped to handle complex workflows and ensure robust pipeline performance through advanced management techniques and versioning practices.