Introduction to Machine Learning Models (AI) Testing

Descripción

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 of Agenda of the tutorial: • Introduction to Artificial Intelligence • Overview of Machine Learning Models and their Lifecycle • Shift-Left Testing in the ML Engineering Phase • QA Functional Testing in the ML Validation Phase • API Testing Scope for Machine Learning Models • Responsible AI Testing for ML Models • Post-Deployment Testing Strategies for ML Models • Continuous Tracking and Monitoring Activities for QA in Production By 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.

Lo que aprenderás

• Introduction to Artificial Intelligence and Machine Learning Models
• Understanding Lifecycle of Machine Learning Models and their testing Scope
• Shift-Left Testing in the ML Engineering Phase such as OverFitting & UnderFitting Testing
• QA Functional Testing in the ML Validation Phase with 25 different Testing types & Strategies
• API Testing Scope for Machine Learning Models with ChatGPT Model example
• Responsible AI Testing for Machine Learning Models such as Bias, Fairness, Ethical, Privacy Testing etc
• Post-Deployment Testing Strategies for ML Models such as DataDrift & Concept Drift testing
• Continuous Tracking and Monitoring Activities for QA in Production

Requisitos

• None. All the concepts are taken care with Scratch explanation