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BharatMLStack

BharatMLStack is an open-source, end-to-end machine learning infrastructure stack built at Meesho to support real-time and batch ML workloads at Bharat scale

Install / Use

/learn @Meesho/BharatMLStack
About this skill

Quality Score

0/100

Category

Operations

Supported Platforms

Universal

README

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/bharatmlstack-logo.png" /> <source media="(prefers-color-scheme: light)" srcset="assets/bharatmlstack-logo.png" /> <img src="assets/bharatmlstack-logo.png" alt="BharatMLStack" width="100%"/> </picture> </div> <div align="center">

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What is BharatMLStack?

BharatMLStack is a production-ready, cloud-agnostic ML infrastructure platform that powers real-time feature serving, model inference, and embedding search at massive scale. Built and battle-tested at Meesho, it is designed to help organizations ship ML to production faster, cheaper, and more reliably.

Our Vision

BharatMLStack is built around four core tenets:

Workflow Integration & Productivity

Ship ML to production faster than ever.

  • 3x faster experiment-to-deployment cycles
  • 95% reduction in model onboarding time

Cloud-Agnostic & Lock-In Free

Run anywhere. Own your stack.

  • Runs across public cloud, on-prem, and edge
  • Kubernetes-native with zero vendor lock-in

Economic Efficiency

Do more with less.

  • 60–70% lower infrastructure costs vs hyperscaler managed services
  • Optimized resource utilization across CPU and GPU workloads

Availability & Scalability

Enterprise-grade reliability at internet scale.

  • 99.99% uptime across clusters
  • 1M+ QPS with low latency

Designed Truly for Bharat Scale

Built for the demands of one of the world's largest e-commerce platforms:

| Metric | Performance | |--------|-------------| | Feature Store | 2.4M QPS (batch of 100 id lookups) | | Model Inference | 1M+ QPS | | Embedding Search | 500K QPS | | Feature Retrieval Latency | Sub-10ms |

Core Components

| Component | Description | Version | Docs | |-----------|-------------|---------|------| | TruffleBox UI | Web console for feature registry, cataloging, and approval workflows | v1.3.0 | Docs | | Online Feature Store | Sub-10ms feature retrieval at millions of QPS with streaming ingestion | v1.2.0 | Docs | | Inferflow | DAG-based real-time inference orchestration for composable ML pipelines | v1.0.0 | Docs | | Numerix | Rust-powered math compute engine for high-performance matrix ops | v1.0.0 | Docs | | Skye | Vector similarity search with pluggable backends | v1.0.0 | Docs | | Go SDK | Go client for Feature Store, Interaction Store, and logging | v1.3.0 | Docs | | Python SDK | Python client libraries for Feature Store and inference logging | v1.0.1 | Docs | | Interaction Store | ScyllaDB-backed store for user interaction signals at sub-10ms | — | — | | Horizon | Control plane that orchestrates all services and powers TruffleBox UI | v1.3.0 | — |

Full documentation at meesho.github.io/BharatMLStack | Blogs

Quick Start

git clone https://github.com/Meesho/BharatMLStack.git
cd BharatMLStack/quick-start
#Set versions
ONFS_VERSION=v1.2.0 HORIZON_VERSION=v1.3.0 TRUFFLEBOX_VERSION=v1.3.0 NUMERIX_VERSION=v1.0.0

./start.sh

For step-by-step setup, Docker Compose details, sample data, and health checks, see the full Quick Start Guide →.

Architecture

<div align="center"> <img src="assets/bharatmlstack-architecture.png" alt="BharatMLStack Architecture" width="1000"/> </div>

Use-Cases

BharatMLStack powers a wide range of ML-driven applications:

| Use-Case | What BharatMLStack Enables | |----------|---------------------------| | Personalized Candidate Generation | Retrieve and rank millions of candidates in real time using feature vectors and embedding similarity | | Personalized Ranking | Serve user, item, and context features at ultra-low latency to power real-time ranking models | | Fraud & Risk Detection | Stream interaction signals and features to detect anomalies and fraudulent patterns in milliseconds | | Image Search | Run embedding search at 500K QPS to match visual queries against massive product catalogs | | LLM Recommender Systems | Orchestrate LLM inference pipelines with feature enrichment for next-gen recommendation engines | | DL & LLM Deployments at Scale | Deploy and scale deep learning and large language models across GPU clusters with Inferflow orchestration |

Contributing

We welcome contributions from the community! Please see our Contributing Guide for details on how to get started.

Community & Support

License

BharatMLStack is open-source software licensed under the BharatMLStack Business Source License 1.1.


<div align="center"> <strong>Built with ❤️ for the ML community from Meesho</strong> </div> <div align="center"> <strong>If you find this useful, ⭐️ the repo — your support means the world to us!</strong> </div>

Related Skills

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GitHub Stars681
CategoryOperations
Updated20h ago
Forks74

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Security Score

85/100

Audited on Mar 27, 2026

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