How to ensure reliability and accuracy of your AI systems and LLMs?
This webinar equips you with data-driven strategies to conquer AI testing challenges. Learn how to build a strong foundation, identify and mitigate bias, and effectively test your AI pipelines.
In the world of AI, data quality is paramount for reliable systems. This webinar delves into the foundations of data quality, addressing the challenges of AI-driven testing, such as bias, hallucinations, and the integration of external language models. Discover various testing techniques, from fuzzing to adversarial testing, and learn how to create synthetic data for robust evaluations. Through real-world examples, case studies, and interactive demos, this session will equip you with the tools and knowledge to master AI testing and achieve clarity from chaos.
Speakers
Aravind Ganesan CDO, Qapitol QA
With two decades of experience in strategizing
and executing Digital Transformation, Portfolio
migrations, and Payment Gateway Integration
projects, Aravind oversees the entire delivery
portfolio and is instrumental in unlocking
superior value for clients through Qapitol’s
solutions and services.
Dheeraj Karanam Director, Middle East (Sales), Qapitol QA
Dheeraj, a skilled problem solver, aids tech firms in strategic product decisions. He assembles adept testing teams delivering comprehensive solutions, benefiting renowned companies like Flipkart and Reliance Retail. He is passionate about merging Techno-Business Skills to elevate software testing for global impact.
This webinar is a must-attend for anyone involved in AI development and testing. Don’t miss this opportunity to take your AI testing to the next level!
Register now and discover how to overcome challenges and enhance testing processes with practical and actionable insights.
Agenda
16th July, 2024
- Foundations of Data Quality for AI
- Challenges in AI-Driven Testing
- Testing Techniques for AI and ML Pipelines
- Maximizing Consistency with External LLMs
- Real-World Examples and Case Studies
- Interactive Demos
