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AI DevSummit 2025 + DeveloperWeek Leadership 2025
Thursday May 29, 2025 2:30pm - 2:55pm PDT
Rachita Naik, Lyft, Machine Learning Engineer

This session will provide a comprehensive, hands-on guide to designing efficient, production-ready data pipelines for machine learning model training. Tailored for engineers, ML practitioners, and architects, this talk will break down key technical aspects of data processing, feature management, and pipeline optimization at scale.
Key takeaways include -
1. Optimized Data Ingestion: Efficiently processing real-time and batch data from multiple sources while minimizing latency and ensuring smooth data flow for ML models.
2. Reusable & Scalable Features: Designing centralized feature stores that enable cross-model sharing, reduce redundancy, and support large-scale ML operations.
3. Robust Data Preprocessing: Implementing techniques to clean, transform, and structure raw data, ensuring high-quality inputs that improve model accuracy and efficiency.
4. Ensuring Data Consistency: Maintaining parity between offline training and real-time inference by preventing schema mismatches, data drift, and inconsistencies.
5. Proactive Monitoring & Debugging: Using automated tracking, anomaly detection, and logging to identify bottlenecks, optimize pipeline performance, and ensure data reliability.

This session will combine technical deep dives with real-world lessons from deploying ML pipelines at scale in rideshare applications. Whether you’re designing your first ML pipeline or optimizing existing workflows, you’ll walk away with practical strategies to enhance data efficiency, model reliability, and overall system performance.
Speakers
avatar for Rachita Naik

Rachita Naik

Machine Learning Engineer, Lyft
Rachita Naik is a Machine Learning (ML) Engineer at Lyft, Inc., and a distinguished graduate of Columbia University. With a strong foundation in AI and two years of professional experience, she is dedicated to creating transformative solutions that address complex, real-world challenges... Read More →
Thursday May 29, 2025 2:30pm - 2:55pm PDT
AI DevSummit Main Stage

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