Aditi Godbole

In this talk, we'll dive into the million-dollar question facing many developers today: Does your problem really need an AI-based solution? We are often excited by the potential of AI, but it's crucial to know when it's the right tool for the job. We'll explore practical criteria to evaluate whether AI is suitable for your specific challenges. This includes assessing problem complexity, data availability, and the importance of explainable outputs. We'll also touch on ethical considerations and the potential costs versus benefits of implementing AI solutions. Drawing from real-world examples, we'll discuss scenarios where AI shines - like tackling complex decision-making, pattern recognition, and automation tasks. We'll also look at situations where traditional programming approaches might be more appropriate. By the end of this session, you'll have a clearer framework for deciding when to leverage AI in your projects and when to stick with conventional methods.

Businesses today want to make data-driven decisions, but face challenges like privacy concerns, limited data availability, or unbalanced datasets, these issues can hinder the development of robust machine learning models. I'll examine how recent advancements in synthetic data and augmentation methods offer promising solutions. The discussion will cover techniques for generating realistic, diverse datasets that mimic real-world data, helping enterprises overcome data scarcity while protecting sensitive information. I'll also cover ways to enhance existing data to improve machine learning models. Through case studies and best practices, I'll provide insights into successful implementation strategies.