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Testing Aiml Models
What you'll learn
Upon completing this course, QA , SDET, QA Test Automation professionals will be equipped to
Effectively Test and Validate ML Models.
Implement ML-Specific API Testing and Automation.
Monitor and Manage Models for Ongoing Quality Assurance
Ensure Responsible and Ethical AI through Rigorous Testing
Requirements
This course is designed to be accessible to both beginners and experienced QA professionals looking to expand their expertise into AI and ML testing. To get the most out of this course, here are a few helpful (but not mandatory) prerequisites
Basic Understanding of Software Testing Principles.
Interest in Machine Learning Concepts. No prior experience with ML is necessary, but a curiosity about how machine learning models work will enhance your learning experience.
Familiarity with Testing Tools (Preferred but Not Required)
A Laptop or Computer for Hands-On Practice.
Description
Course Highlights:Manage the end-to-end lifecycle of ML models. Gain the skills to perform functional, early-stage, and post-deployment testing of ML models.Catch issues early with proactive, early-stage testing.Define and automate API testing for ML models. Learn how to design, and automate API testing for machine learning models.Master functional testing to validate model accuracy and behaviour.Uphold responsible AI through dedicated testing methods. Apply ethical testing techniques to identify biases, ensure model transparency, and uphold fairness in ML applications.Set up continuous quality assurance to monitor live ML models.Why This Course?ML Testing Skills Are in High Demand - Be among the few QA specialists with this expertiseStay Relevant in the Evolving Tech Landscape - AI/ML testing isn't just an edge; it's essentialTransform Your Career Prospects - Add powerful, sought-after skills to your profile!Did you know? As AI and ML continue to transform industries, the need for QA professionals who understand how to test ML models is skyrocketing. Traditional QA methods don't cover ML's complexities like data drift, model accuracy, and ethical concerns, making ML testing a must-have skill for any QA professional looking to stay relevant and impactful.If you're looking to add one of the most in-demand skills to your QA skillset, then this course is a must!
Overview
Section 1: Basics of Machine Learning Model
Lecture 1 Intro to AI system.
Lecture 2 Introduction and agenda of tutorial.
Lecture 3 What is Machine Learning and approaches of machine learning.
Section 2: Early Testing in ML Model Modelling or Engineering Phase.
Lecture 4 ML Model Lifecycle Offline and Online Modes.
Lecture 5 Important Terminologies in ML Model Testing.
Lecture 6 Demo- Predicting Energy output of power plant(ML Model).
Lecture 7 Supervised Learning and Hyperparamters.
Section 3: Unsupervised Learning Models Testing in Modelling phase
Lecture 8 Unsupervised learning and types.
Section 4: Reinforcement Learning in ML models
Lecture 9 Reinforcement Learning in ML models with examples
Lecture 10 Top Python Libraries for ML Models.
Section 5: Functional Testing of AIML model in Evaluation phase.
Lecture 11 Temperature Testing to fine tune the AIML model response
Lecture 12 Zero shot prompting Testing.
Lecture 13 Chain of Thought Prompting Testing.
Lecture 14 Repeatability and Context Management Testing.
Section 6: API Automation
Lecture 15 Download Postman, create Google Gemini Account and setup env for testing.
Lecture 16 PostBot Pluggin to generate automation script for Model API Response.
Section 7: Responsible AI Testing with examples.
Lecture 17 Responsible AI and Fairness and Bias Detection Testing.
Lecture 18 Transparency and Ethical Testing.
Lecture 19 Data Security and Privacy Testing.
Lecture 20 Societal Impact Testing.
Section 8: Post Deployment Testing of AIML Models.
Lecture 21 Latency and Drift Testing.
Lecture 22 Shadow Testing and Canary Testing of AIML Model.
Section 9: ThankYou!!
Lecture 23 Thank You Note!!
This course is tailored for QA professionals, SDETs, Data Analysts, and anyone involved in quality assurance who wants to expand their skills into the exciting field of AI and Machine Learning (ML) testing. The course content is designed to help you bridge the gap between traditional software testing and the specialized needs of ML model validation, making it valuable for:,Quality Assurance (QA) Engineers looking to enhance their testing toolkit with skills specific to AI/ML model reliability, functionality, and fairness.,Software Development Engineers in Test (SDETs) aiming to stay ahead of the curve by learning how to automate and monitor ML model testing processes.,Functional and Automation Testers interested in developing new testing strategies for ML models and ensuring their robust performance across different environments.,Data Analysts and ML Enthusiasts who want to learn the testing practices that can ensure model accuracy and compliance in production settings.
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Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 849.76 MB | Duration: 1h 59m
QA Pros, SDETs, and Functional Testers-Add AI/ML Testing to Your Skill Set ! !
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 849.76 MB | Duration: 1h 59m
QA Pros, SDETs, and Functional Testers-Add AI/ML Testing to Your Skill Set ! !
What you'll learn
Upon completing this course, QA , SDET, QA Test Automation professionals will be equipped to
Effectively Test and Validate ML Models.
Implement ML-Specific API Testing and Automation.
Monitor and Manage Models for Ongoing Quality Assurance
Ensure Responsible and Ethical AI through Rigorous Testing
Requirements
This course is designed to be accessible to both beginners and experienced QA professionals looking to expand their expertise into AI and ML testing. To get the most out of this course, here are a few helpful (but not mandatory) prerequisites
Basic Understanding of Software Testing Principles.
Interest in Machine Learning Concepts. No prior experience with ML is necessary, but a curiosity about how machine learning models work will enhance your learning experience.
Familiarity with Testing Tools (Preferred but Not Required)
A Laptop or Computer for Hands-On Practice.
Description
Course Highlights:Manage the end-to-end lifecycle of ML models. Gain the skills to perform functional, early-stage, and post-deployment testing of ML models.Catch issues early with proactive, early-stage testing.Define and automate API testing for ML models. Learn how to design, and automate API testing for machine learning models.Master functional testing to validate model accuracy and behaviour.Uphold responsible AI through dedicated testing methods. Apply ethical testing techniques to identify biases, ensure model transparency, and uphold fairness in ML applications.Set up continuous quality assurance to monitor live ML models.Why This Course?ML Testing Skills Are in High Demand - Be among the few QA specialists with this expertiseStay Relevant in the Evolving Tech Landscape - AI/ML testing isn't just an edge; it's essentialTransform Your Career Prospects - Add powerful, sought-after skills to your profile!Did you know? As AI and ML continue to transform industries, the need for QA professionals who understand how to test ML models is skyrocketing. Traditional QA methods don't cover ML's complexities like data drift, model accuracy, and ethical concerns, making ML testing a must-have skill for any QA professional looking to stay relevant and impactful.If you're looking to add one of the most in-demand skills to your QA skillset, then this course is a must!
Overview
Section 1: Basics of Machine Learning Model
Lecture 1 Intro to AI system.
Lecture 2 Introduction and agenda of tutorial.
Lecture 3 What is Machine Learning and approaches of machine learning.
Section 2: Early Testing in ML Model Modelling or Engineering Phase.
Lecture 4 ML Model Lifecycle Offline and Online Modes.
Lecture 5 Important Terminologies in ML Model Testing.
Lecture 6 Demo- Predicting Energy output of power plant(ML Model).
Lecture 7 Supervised Learning and Hyperparamters.
Section 3: Unsupervised Learning Models Testing in Modelling phase
Lecture 8 Unsupervised learning and types.
Section 4: Reinforcement Learning in ML models
Lecture 9 Reinforcement Learning in ML models with examples
Lecture 10 Top Python Libraries for ML Models.
Section 5: Functional Testing of AIML model in Evaluation phase.
Lecture 11 Temperature Testing to fine tune the AIML model response
Lecture 12 Zero shot prompting Testing.
Lecture 13 Chain of Thought Prompting Testing.
Lecture 14 Repeatability and Context Management Testing.
Section 6: API Automation
Lecture 15 Download Postman, create Google Gemini Account and setup env for testing.
Lecture 16 PostBot Pluggin to generate automation script for Model API Response.
Section 7: Responsible AI Testing with examples.
Lecture 17 Responsible AI and Fairness and Bias Detection Testing.
Lecture 18 Transparency and Ethical Testing.
Lecture 19 Data Security and Privacy Testing.
Lecture 20 Societal Impact Testing.
Section 8: Post Deployment Testing of AIML Models.
Lecture 21 Latency and Drift Testing.
Lecture 22 Shadow Testing and Canary Testing of AIML Model.
Section 9: ThankYou!!
Lecture 23 Thank You Note!!
This course is tailored for QA professionals, SDETs, Data Analysts, and anyone involved in quality assurance who wants to expand their skills into the exciting field of AI and Machine Learning (ML) testing. The course content is designed to help you bridge the gap between traditional software testing and the specialized needs of ML model validation, making it valuable for:,Quality Assurance (QA) Engineers looking to enhance their testing toolkit with skills specific to AI/ML model reliability, functionality, and fairness.,Software Development Engineers in Test (SDETs) aiming to stay ahead of the curve by learning how to automate and monitor ML model testing processes.,Functional and Automation Testers interested in developing new testing strategies for ML models and ensuring their robust performance across different environments.,Data Analysts and ML Enthusiasts who want to learn the testing practices that can ensure model accuracy and compliance in production settings.
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