Introduction To Machine Learning Models (ai) Testing
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.01 GB | Duration: 4h 54m
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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