The Model Monitoring and Evaluation with MLflow course is designed to equip learners with the tools and techniques required to track, monitor, and evaluate machine learning models effectively using MLflow.
Model Monitoring and Evaluation with MLflow
The Model Monitoring and Evaluation with MLflow course is designed to equip learners with the tools and techniques required to track, monitor, and evaluate machine learning models effectively using MLflow. In the ever-evolving field of AI, it’s not enough to simply build models—ongoing model evaluation and real-time monitoring are crucial for ensuring reliability and performance, especially in production environments.
Through this hands-on course, you'll explore MLflow’s tracking, model management, and deployment capabilities. You'll also learn how to implement advanced model evaluation strategies, compare learning models in machine learning, and apply best practices for monitoring models in production. This course is ideal for anyone seeking to operationalise machine learning pipelines with greater efficiency and transparency.
This course is designed for data scientists, machine learning engineers, DevOps professionals, and AI practitioners who want to gain practical experience in model monitoring and evaluation. It’s also ideal for software engineers and developers looking to integrate ML models into production environments while maintaining control over their performance. Prior experience with machine learning models and Python is recommended, but not strictly required. Familiarity with experiment tracking tools or MLOps concepts will be helpful but not mandatory.
Understand the foundations of Model Monitoring and Evaluation with MLflow
Set up and configure MLflow for experiment tracking
Work with common model evaluation metrics and learn to interpret them
Manage, register, and version models effectively using MLflow’s Model Registry
Implement real-time monitoring of deployed models for performance and accuracy
Perform model comparison and hyperparameter tuning using MLflow
Apply industry best practices for evaluating models in production
Explore advanced techniques for scalable and reliable model monitoring
-
Get an overview of model monitoring concepts, learn the importance of tracking and evaluating models, and understand the role of MLflow in the ML lifecycle.
-
Learn how to install and configure MLflow, connect it with your machine learning projects, and use it to track experiments, parameters, and results.
-
Dive into evaluation metrics for classification and regression models such as accuracy, precision, recall, F1 score, AUC, MSE, and R², and how to use them within MLflow.
-
Explore MLflow’s Model Registry, learn to register models, manage multiple versions, and implement lifecycle stages (Staging, Production, Archived).
-
Discover how to monitor live model performance in real time, detect data drift, and integrate MLflow with other tools like Prometheus or custom logging dashboards.
-
Use MLflow to compare multiple learning models, visualize performance metrics, and track hyperparameter tuning experiments for optimal model selection.
-
Understand production-specific model evaluation techniques, including online vs offline testing, A/B testing, and canary releases.
-
Explore advanced strategies for scalable monitoring, including automated model retraining, continuous evaluation pipelines, and integration with CI/CD tools.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
AlKhajum Husaini
Machine LearningIt's been very informative
No, this course starts with the basics of MLflow and guides you step-by-step, making it suitable for learners new to the platform.
Python is the primary language used, as it is widely adopted in machine learning and well-supported by MLflow.
Yes, the course is fully self-paced, allowing learners to progress at their own speed and revisit content as needed.
MLflow is an open-source platform for managing the complete machine learning lifecycle, including experiment tracking, model management, deployment, and monitoring.
Model evaluation is the process of measuring a machine learning model’s performance using specific metrics to ensure accuracy, reliability, and relevance to the problem domain.
TensorFlow is primarily a deep learning framework used for building and training models, while MLflow is a platform that helps track, manage, and deploy those models. They are often used together in an end-to-end machine learning pipeline.
Key Aspects of Course
Employer approved
Boost your career prospects for free
