Become an Expert in Automated Machine Learning Testing
Published 4/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Automate ML Testing with Python: Master Data Quality, Model Evaluation & CI/CD for Scalable AI Solutions
What you’ll learn
Understand and apply key testing strategies, from data quality checks to model evaluation and automation
Learn to write scripts for data tests, evaluation metrics, and deploy automated pipelines for robust ML testing
Detect and mitigate biases in ML models with hands-on fairness testing and integration into CI/CD workflows
Master metrics like MAE, F1-Score, and Silhouette for effective model evaluation across ML tasks
Conduct stress tests, edge-case validations, and adversarial testing to boost model robustness
Automate monitoring, detect data drift, and set up alerts to maintain model performance in production
Requirements
Familiarity with Python is essential as the course involves writing and automating test scripts
A foundational knowledge of ML/AI concepts like models, data preprocessing, and evaluation is recommended
Having some familiarity with ML libraries like Scikit-learn, TensorFlow, or PyTorch is helpful for the exercises but not essentia
A willingness to explore automated workflows and testing tools is important for hands-on exercises
A learning mindset to tackle challenges in testing ML models and pipelines
Description
Who this course is for
Aspiring ML Engineers: Anyone looking to enhance their ML testing and automation skills
Data Scientists: Professionals who want to ensure robust and reliable machine learning models
Software Testers: Those transitioning into ML testing or seeking to expand their testing expertise
Tech Enthusiasts: Learners with a passion for AI and a curiosity about testing and deployment workflows
ML Developers: Engineers aiming to integrate automated testing into their machine learning pipelines
