Kiruthika Subramani
Google Developer Expert
Montréal, Canada
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Kiruthika Subramani is an aspiring AI professional with a robust foundation in developing and implementing AI solutions. She holds industry certifications in Data, Machine Learning, and Cloud, and is recognized as an IBM Dual Champion and Google Developer Expert. Kiruthika is a prolific speaker and author, having delivered over 200 AI talks and published 100 blogs. She is committed to driving innovation and ethical AI practices, ensuring responsible and impactful advancements in the field.
Kiruthika is currently pursuing a Master’s in Computer Science at Université de Montréal (MILA), Canada, and holds a B.Tech in Artificial Intelligence and Data Science from M.Kumarasamy College of Engineering, Karur, India. Her technical skills span across programming languages (Python, Java, C++), machine learning, deep learning, NLP, computer vision, responsible AI, cloud platforms, and various tools and frameworks.
Authored two books on AI and holds several professional certifications. She has been recognized as a Google Developer Expert, IBM Dual Champion, and Google Cloud Champion Innovator. Her key accomplishments include winning multiple hackathons and receiving the Best Outgoing Student Award. She has also been spotlighted as Today’s Architects by IBM and has delivered numerous global tech talks and authored a popular blog series on Medium
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Topics
Model Chaining for Complex AI Systems
In this session, you will understand how to build complex AI systems using model chaining techniques. I’ll guide you through linking multiple AI models to perform sophisticated tasks, with lab exercises designed to help you create and deploy your own model chains effectively.
The Art of Storytelling with Data
The Art of Storytelling with Data is designed to help participants choose the right visualizations to convey their message effectively. The session covers various visualization types, such as bar charts, line graphs, and scatter plots, explaining when and why to use each one to tell a compelling story. It also delves into the principles of visual storytelling, including how to highlight key insights, avoid common pitfalls, and create emotionally engaging narratives. Participants will learn to tailor their visualizations to different audiences, ensuring clarity and impact. This non-technical session is ideal for anyone looking to transform data into captivating stories that resonate with a broad audience.
Beyond Accuracy - Integrating Responsible AI Metrics and Explainability into Model Evaluation
While machine learning models excel in predictive accuracy, traditional evaluation metrics often overlook critical issues like bias, fairness, and transparency. This talk emphasizes the urgent need for Responsible AI to build trustworthy ML systems. We'll explore common ML challenges, discuss the limitations of traditional evaluation methods, and advocate for incorporating Responsible AI metrics to ensure model robustness. To address the black-box nature of many models, we'll underscore the importance of explainability and provide a live demonstration of Explainable AI techniques to shed light on model decision-making processes.
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