Machine learning and AI have become essential tools for delivering real-time solutions across industries. However, as these technologies scale, they bring their own set of challenges—complexity, data drift, latency, and the constant fight between innovation and reliability. How can we deploy models that not only enhance user experiences but also keep up with changing demands? And what does it take to ensure that these solutions are built to adapt, perform, and deliver value at scale?

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. 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.

In the episode, Adel and Rachita explore how machine learning is leveraged at Lyft, the primary use-cases of ML in ride-sharing, what goes into an ETA prediction pipeline, the challenges of building large scale ML systems, reinforcement learning for dynamic pricing, key skills for machine learning engineers, future trends across machine learning and generative AI and much more.

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Links Mentioned in the Show:
Engineering at Lyft on Medium – https://eng.lyft.com/
Connect with Rachita – https://www.linkedin.com/in/rachita-naik98/
Research Paper—A Better Match for Drivers and Riders: Reinforcement Learning at Lyft – https://arxiv.org/pdf/2310.13810
Career Track: Machine Learning Engineer – https://www.datacamp.com/tracks/machine-learning-engineer
Related Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author – https://www.datacamp.com/podcast/why-ml-projects-fail-and-how-to-ensure-success-with-eric-siegel-founder-of-machine-learning-week
Sign up to RADAR: Forward Edition – https://www.datacamp.com/radar-forward-edition-2024

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