ML ARCHITECTURE

Enterprise-Grade ML Architecture

Design, deploy, and manage scalable Machine Learning pipelines and infrastructure. Transform your ML models from notebooks to production-ready systems.

99.9% model uptime

10x faster deployment

Scalable infrastructure

Scale Your AI

Production-Ready ML Systems

Moving from notebook to production requires robust engineering. We build the infrastructure that keeps your models running reliably, securely, and at scale. Our MLOps expertise ensures your machine learning systems are maintainable, monitored, and continuously improved.

From model versioning and experiment tracking to automated retraining pipelines and model serving infrastructure, we handle the complete ML lifecycle. Focus on innovation while we manage the complexity of production ML systems.

Core Services:

MLOps & CI/CD for ML
Model Serving & APIs
Scalable Data Pipelines
Model Monitoring & Observability
Feature Stores
Experiment Tracking

// ML Pipeline
pipeline
.deploy({
                        model: 'v2.3.1',
replicas: 5,
monitoring: true,
autoscale: enabled

                        });
                    

MLOps Capabilities

Model Versioning

Track, version, and manage all your ML models with complete lineage and reproducibility.

Automated Deployment

CI/CD pipelines that automatically test, validate, and deploy models to production.

Model Monitoring

Real-time performance tracking, drift detection, and automated alerting.

Feature Engineering

Centralized feature stores with versioning and sharing across teams.

Low-Latency Serving

High-performance model serving infrastructure with auto-scaling capabilities.

Auto Retraining

Scheduled and trigger-based model retraining to maintain accuracy over time.

Implementation Roadmap

Infrastructure Assessment (Week 1-2)

Audit existing ML workflows, identify bottlenecks, and design scalable architecture.

MLOps Platform Setup (Week 3-4)

Deploy experiment tracking, model registry, and feature store infrastructure.

Pipeline Development (Week 5-7)

Build automated training, evaluation, and deployment pipelines with CI/CD.

Model Deployment (Week 8-9)

Deploy models to production with monitoring, logging, and auto-scaling.

Optimization & Handoff (Week 10-12)

Fine-tune performance, train team on MLOps tools, and ensure smooth operations.

Pricing Tiers

Foundation

₹49,999

Essential MLOps infrastructure

Basic MLOps setup
Model versioning & tracking
Simple deployment pipeline
60 days support

Get Started

Production

₹79,999

Full production ML platform

Complete MLOps platform
Feature store & data pipelines
Advanced monitoring & alerts
90 days support