Building Robust AI Products
What you'll learn
- Apply best practices to design resilient AI systems
- Evaluate data quality, diversity, and bias in AI training
- Identify and mitigate risks in AI supply chains
- Build a roadmap for secure, compliant AI deployment
Requirements
- Basic understanding of AI concepts and machine learning fundamentals
- An interest in AI product development, governance, or risk management
Description
Are you building AI products that need to operate reliably in the real world? Do you want to ensure your AI systems are secure, resilient, and aligned with regulatory expectations?
This course is your complete guide to building robust AI products—from concept to continuous improvement.
Whether you're an AI product manager, data scientist, tech leader, or ML engineer, this course will equip you with the frameworks and real-world practices to design, deploy, and maintain AI solutions that perform consistently and responsibly.
What You’ll Learn:
How to evaluate data quality, coverage, bias, and robustness
Ways to avoid brittle models through smart data and architecture choices
How to manage risks from AI supply chains, vendors, and third-party models
Techniques for testing AI across multiple use cases and data scenarios
Methods for identifying and defending against prompt injection and adversarial attacks
The principles of AI governance and aligning with GDPR, ISO 27701, and NIST
Secure deployment practices and continuous monitoring strategies
How to build an actionable AI product roadmap
Each concept is illustrated using GenAI Assist, a model AI system built by IntelliTech Solutions. This helps you apply lessons directly to real-world use cases in legal, financial, and compliance-focused AI solutions.
You’ll complete hands-on labs, interactive quizzes, and a final project where you’ll create your own Resilient AI Product Roadmap—a practical plan you can apply to any AI project.
Who This Course Is For:
AI product managers and tech leads seeking real-world deployment strategies
Data scientists and ML engineers looking to improve AI reliability and fairness
Security professionals involved in AI red teaming and threat mitigation
Business and compliance leaders responsible for AI accountability
No coding required. Whether you're leading AI development or managing its risks, this course helps you deliver AI systems that are trustworthy, scalable, and secure.
Join us and start building AI that lasts.
Who this course is for:
- Tech leaders, AI product managers, and startup founders seeking to build trustworthy AI systems
- Data scientists and ML engineers interested in production-ready AI strategies
- Security professionals and compliance teams involved in AI governance
- Anyone responsible for launching, scaling, or securing AI products in real-world environments
Instructor
- 4.5 Instructor Rating
- 15,522 Reviews
- 104,646 Students
- 330 Courses
PhD in computer science and IT manager with 35 years technical experience in various fields including IT Security, IT Governance, IT Service Management , Software Development, Project Management, Business Analysis and Software Architecture. I hold 80+ IT certifications such as :
ITIL 4 Master, ITIL 3 Expert
ISO 27001 Auditor, ComptIA Security+, GSEC, CEH, ECSA, CISM, CISSP, CISA
PGMP, MSP
PMP, PMI-ACP, Prince2 Practitioner, Praxis, Scrum Master
COBIT 2019 Implementor, COBIT 5 Assessor/Implementer
TOGAF certified
Lean Specialist, VSM Specialist
PMI RMP, ISO 31000 Risk Manager, ISO 22301 Lead Auditor
PMI-PBA, CBAP
Lean Six Sigma Black Belt, ISO 9001 Implementer
Azure Administrator, Azure DevOps Expert, AWS Practitioner
And many more.