Meesho Opens Up Machine Learning Platform BharatMLStack to India's AI Community
E-commerce giant Meesho has open-sourced key components of its internal machine learning platform, BharatMLStack, on GitHub. The company aims to foster collaboration and improve the platform through community feedback, rather than monetizing the release.
BharatMLStack was developed over the past two to three years to support real-time machine learning applications across Meesho's systems. It has been rigorously tested, including during the March 2025 Mega Blockbuster Sale, where it performed well under high-traffic conditions. The platform is engineered to accommodate uniquely Indian internet behaviors such as transliterated search queries, low-resource devices, and fuzzy product discovery.
The initial open-source release includes a production-grade feature store, control plane, orchestration UI, and SDKs. These components allow for real-time feature serving, model deployment, and user-friendly workflow management. Meesho's Online Feature Store, which serves precomputed features to ML models in real time, is among the released components. Additional components, such as model serving infrastructure and workflow authoring tools, are expected to be open-sourced in phases over the coming months.
In fiscal year 2025, BharatMLStack processed an average of 1.91 petabytes of data per day, executed 66.9 trillion feature retrievals, and enabled 3.12 trillion real-time inferences during peak loads. Meesho hopes that by open-sourcing BharatMLStack, it can equip India's AI and data science community with scalable and financially sustainable tools, similar to how other tech giants like Tata Consultancy Services (TCS), Infosys, and Wipro have contributed to the expansion of India's AI ecosystem.
Meesho's open-source release of BharatMLStack marks a significant step in democratizing access to advanced machine learning tools in India. By sharing its internal platform, Meesho invites collaboration and improvement from the community, with the ultimate goal of fostering a more robust AI ecosystem nationwide.
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