Aditi Godbole
Data Science, AI & ML Leader - SAP
Seattle, Washington, United States
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Aditi Godbole is a passionate Data Science, AI and ML professional with over 11 years of experience in the field. She specializes in using advanced machine learning techniques to address complex business challenges, drive revenue growth, and enhance operational efficiency. Throughout her career, Aditi has led the design, development, and successful execution of large-scale enterprise software projects, with a particular focus on natural language processing and machine learning.
Currently serving as a Senior Data Scientist at SAP, Aditi plays a key role in shaping AI and machine learning strategies. She delivers data-driven solutions for integrated spend management across SAP's product portfolio. Her expertise encompasses a wide range of machine learning disciplines, including supervised learning, natural language processing (NLP), and Generative AI.
Beyond her technical skills, Aditi is dedicated to mentoring and sharing her knowledge to foster growth in others within the Machine Learning and Data Science fields. Aditi's commitment to innovation in the AI/ML field is evident through her patents and active involvement in the industry.
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To AI or Not to AI: Is It the Right Solution for Your Problem?
In this talk, I'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.
Leveraging Synthetic Data to Enhance Enterprise Data
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.
Data Privacy in LLMs: Challenges and Best Practices
This talk explores data privacy challenges in large language models (LLMs). I'll cover LLM basics, AI data privacy fundamentals, and specific privacy issues in LLMs. The discussion will address compliance hurdles, technical solutions for privacy preservation, and their limitations. I'll review industry best practices and examine the balance between innovation, privacy, and business growth. The presentation will conclude with future directions for privacy in AI, providing attendees with insights to navigate these challenges in their AI initiatives.
Aditi Godbole
Data Science, AI & ML Leader - SAP
Seattle, Washington, United States
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