Data Engineering & MLOps

Building robust data infrastructure and MLOps practices that enable reliable AI and analytics at scale. We design data pipelines, implement ML operations, and create the foundation for data-driven decision making and AI deployment.

Who This Is For

Data leaders, ML engineers, and technical teams looking to modernize data infrastructure, operationalize machine learning models, or scale AI initiatives from prototype to production.

Deliverables

What We Deliver

Data Pipeline Architecture
Scalable ETL/ELT pipelines for ingesting, transforming, and serving data from diverse sources with proper orchestration and monitoring.
ML Infrastructure
Production-grade ML infrastructure including model training, versioning, deployment, and serving with auto-scaling capabilities.
CI/CD for ML
Automated testing, validation, and deployment pipelines for machine learning models with rollback capabilities and A/B testing.
Model Monitoring
Real-time monitoring of model performance, data drift, and prediction quality with automated alerting and retraining triggers.
Feature Engineering
Feature stores, transformation pipelines, and experimentation frameworks to accelerate model development and ensure consistency.
Data Governance
Data quality frameworks, lineage tracking, access controls, and compliance measures for secure, reliable data operations.

Real-World Examples

Example Engagements

Enterprise Data Platform

Designed and implemented modern data platform for Fortune 500 retailer, migrating from legacy systems to cloud-native architecture. Built real-time and batch pipelines processing 10TB+ daily.

Outcome

Reduced data processing time by 80%, enabled real-time analytics, cut infrastructure costs by 40%, supported 200+ data scientists.

MLOps Platform Implementation

Built end-to-end MLOps platform for fintech company, enabling data scientists to train, deploy, and monitor models independently. Implemented automated retraining and model registry.

Outcome

Reduced model deployment time from weeks to hours, 10x increase in models in production, 99.9% uptime.

Real-Time Analytics Pipeline

Developed streaming data pipeline for IoT company processing sensor data from 100K+ devices. Implemented real-time anomaly detection and predictive maintenance models.

Outcome

Processing 1M events/second with <100ms latency, 95% accuracy on failure prediction, $2M annual savings.

Investment

Timeline & Pricing

Typical Timeline

Data and MLOps engagements typically run 10-20 weeks depending on scope and existing infrastructure. We follow phased approach: assessment, architecture design, implementation, and optimization. Includes knowledge transfer and documentation.

Pricing Model

Project-based pricing starting at pricing on request for data pipeline implementation, pricing on request for comprehensive MLOps platforms. Ongoing support and optimization available at pricing on request/month. Cloud infrastructure costs additional and optimized for cost-efficiency.

Success Stories

Related Case Studies

See how we've helped other clients achieve their goals with similar services.

Enterprise Data Platform Modernization

Real-Time ML Inference System

Ready to get started?

Let's discuss how data engineering & mlops can help your organization achieve its goals.

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