Data Engineering & MLOps — The Infrastructure That Makes AI Actually Work in Production
Quecko builds the data pipelines, feature stores, model deployment systems, and monitoring infrastructure that turn experimental ML models into reliable, scalable production systems. We engineer the unsexy infrastructure layer that determines whether your AI initiative succeeds or stalls.
Your data scientists built a great model in a notebook. Now what?
The gap between a working Jupyter notebook and a production ML system is enormous — and it's where most AI initiatives die. Production ML requires clean, reliable data pipelines that run on schedule without breaking, feature stores that serve consistent features to training and inference, deployment infrastructure that handles traffic spikes and model versions, and monitoring that detects drift before accuracy collapses. Most organizations have data scientists who can build models but lack the data engineering and MLOps expertise to productionize them. Quecko bridges that gap — we build the infrastructure that turns experiments into production AI systems.
Engineering Production-Grade Data Engineering & MLOps Services Infrastructure
Data & ML Infrastructure Audit
We assess your current data sources, pipelines, storage, and ML workflows. We identify bottlenecks, reliability gaps, and architecture improvements.
Pipeline & Infrastructure Build
Data pipeline development, feature store construction, model serving infrastructure, and integration with your existing data and ML tools.
MLOps Implementation
CI/CD for ML models, automated retraining pipelines, model registry, A/B testing infrastructure, and monitoring/alerting setup.
Production Hardening & Handover
Performance optimization, disaster recovery, documentation, team training, and handover with runbooks for ongoing operations.
Comprehensive Data Engineering & MLOps
Explore our technical specialties, engineering practices, and developer skills.
Data Pipeline Architecture
End-to-end data pipelines — ingestion, transformation, validation, and loading — for batch and real-time processing. We build pipelines that are reliable, testable, observable, and handle schema evolution gracefully.
Feature Store Development
Centralized feature engineering and serving infrastructure. We build feature stores that ensure consistency between training and inference, reduce feature computation redundancy, and enable feature reuse across models.
ML Model Deployment & Serving
Production model serving infrastructure — REST APIs, batch inference, real-time streaming inference, A/B testing, canary deployments, and model versioning. We deploy models that handle production traffic reliably.
Model Monitoring & Drift Detection
Continuous monitoring of model performance, data drift, concept drift, and prediction quality. We build alerting systems that detect degradation before it impacts business outcomes.
Automated Retraining Pipelines
End-to-end ML pipelines that automatically retrain models on fresh data, evaluate against benchmarks, and promote to production — with human approval gates for critical systems.
Data Warehouse & Lake Architecture
Modern data stack design — data warehouses (Snowflake, BigQuery), data lakes (S3/GCS + Delta Lake), and lakehouses. We build architectures that serve both analytics and ML workloads.
Is This Service a Fit for You?
Ideal Match
- ✓Companies with data science teams that need production infrastructure, enterprises modernizing legacy data systems, startups building data-intensive AI products, or ML teams struggling with model deployment and monitoring.
Not a Fit
- —Teams needing a basic SQL dashboard — we build production data and ML infrastructure, not reporting tools.
From Day 1 to Day 90: What Data Engineering & MLOps Execution Looks Like
How we take your Data Engineering & MLOps Services requirements from day 1 to production delivery.
Current infrastructure assessment, data source mapping, pipeline bottleneck identification, and target architecture design.
Data pipelines, feature store, model serving infrastructure, data warehouse/lake architecture, and integration connectors.
CI/CD for ML, retraining pipelines, model registry, monitoring dashboards, drift detection, and end-to-end pipeline testing.
Performance optimization, disaster recovery, load testing, documentation, team training, and operational runbooks.
Day 1–15 — Audit & Architecture
Current infrastructure assessment, data source mapping, pipeline bottleneck identification, and target architecture design.
Day 16–45 — Core Build
Data pipelines, feature store, model serving infrastructure, data warehouse/lake architecture, and integration connectors.
Day 46–75 — MLOps & Testing
CI/CD for ML, retraining pipelines, model registry, monitoring dashboards, drift detection, and end-to-end pipeline testing.
Day 76–90 — Hardening & Handover
Performance optimization, disaster recovery, load testing, documentation, team training, and operational runbooks.
Technologies We Master
Tools, frameworks, and protocols we use to build secure and scalable solutions.
Data Pipelines
Apache Airflow
Dagster
Prefect
dbt
Apache Spark
FlinkFeature Stores
Feast
Tecton
custom feature storesML Platforms
MLflow
Kubeflow
Weights & Biases
AWS SageMaker
GCP Vertex AIModel Serving
TensorFlow Serving
Triton
BentoML
FastAPI
custom inference APIsData Storage
Snowflake
BigQuery
Redshift
Delta Lake
Apache Iceberg
PostgreSQLInfrastructure
Docker
Kubernetes
Terraform
AWS
GCP
AzureWe Build the Infrastructure That Makes Your AI Investments Pay Off.
We don't build experiments — we build infrastructure designed for reliability, scalability, and long-term maintenance from day one.
Production-First Mindset
We don't build experiments — we build infrastructure designed for reliability, scalability, and long-term maintenance from day one.
End-to-End Coverage
From raw data ingestion to model serving and monitoring — we build the complete stack, not just one component.
Engineering Discipline
Our data infrastructure is version-controlled, tested, documented, and monitored — with the same rigor we bring to blockchain and enterprise software engineering.
250+ Products Shipped
We've built production data systems that process millions of records and serve models at scale — not just proof-of-concept pipelines.
Our Work
Our Projects
“With hard work, determination, and an amazing team at Quecko, we can overcome any obstacle and achieve anything we set our minds to.”
Mateen O Dawood
Stable33 ProtocolHow We Collaborate
Dedicated Data & MLOps Pod
Full-time data engineers, ML engineers, and DevOps building your production data and ML infrastructure.
Project-Based Infrastructure Build
Fixed-scope data pipeline, feature store, or MLOps implementation from audit to production.
Infrastructure Audit & Roadmap
Standalone engagement for assessing your current data/ML infrastructure and designing a modernization roadmap.
Frequently Asked Questions
Latest Stories from Quecko
Ready to build the data infrastructure your AI needs to succeed?
From data pipelines and feature stores to model deployment and monitoring — Quecko builds the production infrastructure that turns ML experiments into reliable, scalable AI systems.