Introduction to Machine Learning Models (AI) Testing

Overview

From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples What you'll learn:

Introduction to Artificial Intelligence and Machine Learning ModelsUnderstanding Lifecycle of Machine Learning Models and their testing ScopeShift-Left Testing in the ML Engineering Phase such as OverFitting & UnderFitting TestingQA Functional Testing in the ML Validation Phase with 25 different Testing types & StrategiesAPI Testing Scope for Machine Learning Models with ChatGPT Model exampleResponsible AI Testing for Machine Learning Models such as Bias, Fairness, Ethical, Privacy Testing etcPost-Deployment Testing Strategies for ML Models such as DataDrift & Concept Drift testingContinuous Tracking and Monitoring Activities for QA in Production This course will introduce you to the World of Machine Learning Models Testing. As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations.

However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them.This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples.Below is high level ofAgenda of the tutorial:

Introduction to Artificial IntelligenceOverview of Machine Learning Models and their LifecycleShift-Left Testing in the ML Engineering PhaseQA Functional Testing in the ML Validation PhaseAPI Testing Scope for Machine Learning ModelsResponsible AI Testing for ML ModelsPost-Deployment Testing Strategies for ML ModelsContinuous Tracking and Monitoring Activities for QA in ProductionBy the end of this course,you will gain expertise in testing Machine Learning Models at every stage of their lifecycle.Please Note:

This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples.No specific programming language or code is involved in this tutorial.

Syllabus

• Introduction to Machine Learning Testing
Overview of Machine Learning and AI
Importance of Testing in Machine Learning
• Types of Machine Learning Models
Supervised Learning Models
Unsupervised Learning Models
Reinforcement Learning Models
• Basics of Model Testing
Test Data vs. Training Data
Cross-Validation Techniques
Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
• Techniques for Model Validation
Understanding Overfitting and Underfitting
Bias-Variance Tradeoff
K-Fold Cross-Validation
• Testing Frameworks and Tools
Introduction to Popular Testing Frameworks (e.g., PyTest, UnitTest for Python)
Specific Tools for Machine Learning Testing (e.g., MLflow, TensorFlow Model Analysis)
• Performance Testing
Latency and Throughput
Scaling Machine Learning Models
• Security and Bias Testing
Testing for Model Bias
Security Concerns and Adversarial Testing in AI
• Continuous Integration and Deployment in ML
Implementing CI/CD Pipelines for Machine Learning
Automation in Model Testing and Deployment
• Case Studies and Practical Applications
Real-World Examples of Model Testing
Hands-On Projects and Exercises
• Future Trends in AI Model Testing
Innovations in Testing Methodologies
Evolution of AI Testing with New Technologies
• Conclusion and Review
Recap of Key Concepts
Preparing for Next Steps in Machine Learning Testing

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Taught by

Rahul Shetty

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Subjects

Programming