Accelerating ML Development through Coupang (opens in new tab)

Coupang’s internal Machine Learning (ML) platform serves as a standardized ecosystem designed to accelerate the transition from experimental research to stable production services. By centralizing core functions like automated pipelines, feature engineering, and scalable inference, the platform addresses the operational complexities of managing ML at an enterprise scale. This infrastructure allows engineers to focus on model innovation rather than manual resource management, ultimately driving efficiency across Coupang’s diverse service offerings.

Addressing Scalability and Development Bottlenecks

• The platform aims to drastically reduce "Time to Market" by providing "ready-to-use" services that eliminate the need for engineers to build custom infrastructure for every model.
• Integrating Continuous Integration and Continuous Deployment (CI/CD) into the ML lifecycle ensures that updates to data, code, and models are handled with the same rigor as traditional software engineering.
• By optimizing ML computing resources, the platform allows for the efficient scaling of training and inference workloads, preventing infrastructure costs from spiraling as the number of models grows.

Core Services of the ML Platform

• Notebooks and Pipelines: Integrated Jupyter environments allow for ad-hoc exploration, while workflow orchestration tools enable the construction of reproducible ML pipelines.
• Feature Engineering: A dedicated feature store facilitates the reuse of data components and ensures consistency between the features used during model training and those used in real-time inference.
• Scalable Training and Inference: The platform provides dedicated clusters for high-performance model training and robust hosting services for real-time and batch model predictions.
• Monitoring and Observability: Automated tools track model performance and data drift in production, alerting engineers when a model’s accuracy begins to degrade due to changing real-world data.

Real-World Success in Search and Pricing

• Search Query Understanding: The platform enabled the training of Ko-BERT (Korean Bidirectional Encoder Representations from Transformers), significantly improving the accuracy of search results by better understanding customer intent.
• Real-time Dynamic Pricing: Using the platform’s low-latency inference services, Coupang can predict and adjust product prices in real-time based on fluctuating market conditions and inventory levels.

To maintain a competitive edge in e-commerce, organizations should transition away from fragmented, ad-hoc ML workflows toward a unified platform that treats ML as a first-class citizen of the software development lifecycle. Investing in such a platform not only speeds up deployment but also ensures the long-term reliability and observability of production models.