Design secure, reliable, and compliant AI systems with best practices in data quality, and risk mitigation
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
