USING AI & MACHINE LEARNING
IN THE ENTERPRISE
Miami + ON-DEMAND | September 18, 2024
Rachita Naik
Machine Learning Engineer at Lyft
Rachita Naik is a Machine Learning (ML) Engineer at Lyft, Inc., and a recent graduate of Columbia University in New York. With two years of professional experience, Rachita is dedicated to creating impactful software solutions that leverage the power of Artificial Intelligence (AI) to solve real-world problems. Her academic projects, including work on Diabetic Retinopathy Detection and Breast Cancer Classification and Treatment Planning, have been published in reputed journals and conferences, showcasing her commitment to using technology for meaningful impact.
At Lyft, Rachita focuses on developing and deploying robust ML models to enhance the ride-hailing industry’s pickup time reliability. She thrives on the challenge of addressing ML use cases at scale in dynamic environments, which has provided her with a deep understanding of practical challenges and the expertise to overcome them. Throughout her academic and professional journey, Rachita has honed a diverse skill set in AI and software engineering and remains eager to learn about new technologies and techniques to improve the quality and effectiveness of her work. She is also a part of the Women in Data Science organization, where she is committed to fostering knowledge-sharing, community-building, and empowerment for all women in the field.
Watch in-person: September 18
Deploying Machine Learning Models at Scale
The session will explore the complexities of developing and deploying Machine Learning (ML) models at scale in large tech enterprises. The presentation will provide an in-depth examination of the unique challenges faced – ranging from data management and model training to deployment and monitoring, using real-world use cases to illustrate how vast amounts of data are handled and model performance is maintained in dynamic production environments. Attendees will gain practical knowledge and actionable insights, including effective data management strategies, best practices for ML development and deployment, and tips for leveraging advanced tools and frameworks to streamline the ML process, making it more efficient and effective within their own companies.