Overview

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Building machine learning models is only the first step. To create reliable ML systems, engineers must evaluate model performance, diagnose prediction errors, and monitor deployed models over time. In this course, you'll learn how to train, evaluate, and monitor machine learning models using practical engineering techniques. You’ll begin by exploring model training strategies that improve convergence and performance. You’ll analyze training logs, loss curves, and class imbalance effects to understand how models learn and where they struggle. Next, you’ll learn how to evaluate machine learning models using appropriate performance metrics. You’ll analyze confusion matrices and residual patterns to identify systematic prediction errors and assess the statistical significance of model improvements. Finally, you’ll focus on monitoring machine learning models in production environments. You’ll apply validation techniques, analyze A/B testing results, and monitor model behavior over time to detect performance drift and trigger retraining workflows. Through a hands-on project, you'll design a model evaluation and monitoring framework that helps ensure machine learning systems remain accurate and reliable after deployment.

Syllabus

Model Training & Evaluation: Mini-Batch Training for Better Model Convergence

You will apply batch and mini-batch training procedures to optimize model convergence.

Model Training & Evaluation: Diagnosing Training Issues with Logs and Loss Curves

You will analyze training logs and loss curves to diagnose common model training issues.

Model Training & Evaluation: Comparing Class-Imbalance Techniques in Model Evaluation

You will evaluate the impact of class-imbalance techniques on model performance.

Evaluate, Analyze, and Model Performance: Choosing the Right Performance Metrics

You will apply appropriate performance metrics to evaluate machine learning models.

Evaluate, Analyze, and Model Performance: Diagnosing Model Failures with Error Analysis

You will analyze confusion matrices and residual plots to identify systematic model prediction errors.

Evaluate, Analyze, and Model Performance: Testing Whether Performance Differences Are Significant

You will evaluate the statistical significance of differences in metrics.

Validate, Analyze, and Monitor ML Models: Validating Models on Unseen Data

You will apply validation techniques to assess model performance on unseen data.

Validate, Analyze, and Monitor ML Models: Analyzing Online Experiments and Shadow Deployments

You will analyze A/B test or shadow deployment results to compare new model performance against a baseline.

Validate, Analyze, and Monitor ML Models: Monitoring Model Drift and Triggering Retraining

You will evaluate model-drift indicators to trigger retraining workflows.

Project: End-to-End Model Evaluation & Monitoring Framework

In this project, you will design and implement a machine learning model evaluation and monitoring framework for a production system. A technology company has deployed a recommendation model that predicts user engagement with content, but its performance has become inconsistent due to potential data drift and evolving user behavior. Your task is to build an evaluation pipeline that compares model versions, analyzes prediction errors, and monitors performance stability over time. You will train baseline and improved models, analyze training logs and loss curves to verify convergence, evaluate class-imbalance handling techniques to ensure fair evaluation across classes, evaluate them using appropriate metrics, analyze errors with confusion matrices and residual plots, perform statistical comparisons, simulate monitoring scenarios such as A/B testing or shadow deployments, calculate drift indicators like Population Stability Index (PSI), and define conditions for model retraining. The final deliverable is a modular Python evaluation framework along with a written engineering explanation demonstrating how evaluation insights support reliable model deployment decisions.