In a move aimed at unlocking access to AI infrastructure, Meesho has open-sourced key components of its internal machine learning platform, “BharatMLStack,” on GitHub — including its feature store, control plane, orchestration UI, and SDKs. With this initiative, Meesho is among the first scaled e-commerce players in India to take a step forward toward AI development.
Developed over the past two years, BharatMLStack, Meesho’s Machine Learning Platform, processes large volumes of data every day. In the financial year 2024- 2025, our machine learning systems processed ~1.91 PB data daily on average in Fiscal 2025, resulting in 66.90 trillion features retrievals (data signals fetched to make real time predictions) and 3.12 trillion inferences (real-time predictions made by ML models) at peak.
“We believe great technology should scale impact, not just infrastructure. We put BharatMLStack to the test during high-traffic events like our Mega Blockbuster Sale in March 2025, where it delivered at scale—demonstrating its ability to perform under peak load conditions. This helped Meesho drive higher user engagement, better conversions, and increased order volumes during the sale. By open-sourcing it, we’re sharing a high-scale, AI stack with the broader tech community—purpose-built for real-time use cases and tailored for Indian businesses.”, said Sanjeev Kumar, Founder & CTO, Meesho.
An Online Feature Store helps AI systems by serving precomputed, up-to-date features instantly for real-time model predictions, enabling use cases like fraud detection and personalized recommendations to run faster and more accurately. By sharing this with the broader tech ecosystem, Meesho aims to empower startups, ML engineers, and data scientists across India with access to production-ready, cost-efficient AI tooling tailored for India’s fast-evolving digital economy. Teams can collaborate more effectively, avoid data silos, and quickly build or enhance AI models with consistent, high-quality features, all while accelerating deployment and driving innovation. While it doesn’t replace model development, it significantly simplifies training, deployment and infrastructure management, allowing data scientists and engineers to focus on what matters most: building better models and accelerating innovation.
The Online Feature Store is now available on GitHub, with opportunities for the community to contribute to future components as they are released.
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