Introduction To Machine Learning Models (AI) Testing

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 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

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

Description 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 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.

Overview Section 1: Getting Started with Machine Learning Testing basics

Lecture 1 Introduction and Agenda of the tutorial

Lecture 2 Introduction to Artificial Intelligence Systems with examples

Lecture 3 What is Machine Learning and how it is related to Artificial Intelligence family

Lecture 4 Examples of commonly used Machine Learning Models and their usage

Section 2: Shift Left Testing in ML Model Engineering phase (Supervised Learning)

Lecture 5 Understand Machine Learning Model Life cycle stages with online/offline modes

Lecture 6 How Machine Learning models works in nutshell -Learn terminologies used

Lecture 7 Understand how OverFitting Testng & UnderFitting works with Trained data sets

Lecture 8 Predicting House Prices (ML Model) Demo to show how internally Algorithms works

Lecture 9 Revision on Supervised Learning Model Testing with Overfitting/UnderFitting ex

Section 3: Unsupervised Learning Models Testing in Engineering Phase

Lecture 10 Introduction to Unsupervised Learning in the ML models with example

Lecture 11 Testing scope on Unsupervised Learning with Data point patterns&Cluster scores

Lecture 12 Revision on Unsupervised Learning with cluster score analysis

Section 4: Reinforcement Learning & Commonly used Frameworks and Algos in ML Models

Lecture 13 Introduction to Reinforcement Learning in ML Model with examples

Lecture 14 Algorithms and Frameworks commonly used in developing ML Models

Section 5: Functional Testing for Machine Learning Models in Evaluation Phase

Lecture 15 What are Validation Unseen Data sets and why it is required

Lecture 16 Temperature Testing to fine tune the response predictions from ML Models

Lecture 17 Prompts Testing with Zero Shot & Chain of thought Prompts test

Lecture 18 Relevance stary Testing & Fantasy claims testing on ML Models

Lecture 19 Repeatability Testing & Asking question in different phases to test

Lecture 20 Style Transfer testing & Intent recognition testing on ML Models

Lecture 21 What is Invariance Testing & BiDirectional testing for AI Models

Section 6: Introduction to API Testing on Machine Learning Models

Lecture 22 Create OpenAI Account to test ChatGPT API’s of generating response

Lecture 23 Download Postman tool to setup ChatGPT APIs environment for testing

Lecture 24 PostBot plugin to generate automation scripts for API responses in Postman

Section 7: Responsible AI Testing with examples on Machine Learning (AI) Models

Lecture 25 Importance of Fairness testing on ML responses to check bias

Lecture 26 Transparency testing and why it is necessary to stay ahead in AI competition

Lecture 27 Data Privacy and Security testing on Machine Learning models

Section 8: Post Deployment Testing Types with examples on Machine Learning Models

Lecture 28 Importance of Integration & Latency testing on Production ML models

Lecture 29 Importance of Data drift Testing & Concept Drift Testing in ML Models

Lecture 30 Shadow Testing & A/B Testing to certify the latest version of ML into prod

Section 9: Final words – Impact of Machine Learning Models in QA Space

Lecture 31 How QA’s can be critical resources for Machine Learning Model Life cycle

Lecture 32 Bonus Lecture

QA Testers,Software Engineers,Software Testers,Data Engineers,Developers,Test Managers

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