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Data Engineering & MLOps Services

Data Engineering & MLOps

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.

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400+clients across 20+ countries
$300M+in funds generated
250+products built
150+engineers worldwide
400+clients across 20+ countries
$300M+in funds generated
250+products built
150+engineers worldwide
400+clients across 20+ countries
$300M+in funds generated
250+products built
150+engineers worldwide
400+clients across 20+ countries
$300M+in funds generated
250+products built
150+engineers worldwide
What We Build

Comprehensive Data Engineering & MLOps

01

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.

02

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.

03

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.

04

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.

05

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.

06

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.

The Challenge

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.

Execution Blueprint

From Day 1 to Day 90: What Data Engineering & MLOps Execution Looks Like

01Day 1–15

Audit & Architecture

Current infrastructure assessment, data source mapping, pipeline bottleneck identification, and target architecture design.

02Day 16–45

Core Build

Data pipelines, feature store, model serving infrastructure, data warehouse/lake architecture, and integration connectors.

03Day 46–75

MLOps & Testing

CI/CD for ML, retraining pipelines, model registry, monitoring dashboards, drift detection, and end-to-end pipeline testing.

04Day 76–90

Hardening & Handover

Performance optimization, disaster recovery, load testing, documentation, team training, and operational runbooks.

Technology

Technologies We Master

Tools, frameworks, and protocols we use to build secure and scalable solutions.

Data Pipelines

Apache AirflowApache Airflow
DagsterDagster
PrefectPrefect
dbtdbt
Apache SparkApache Spark
FlinkFlink

Feature Stores

FeastFeast
TectonTecton
custom feature storescustom feature stores

ML Platforms

MLflowMLflow
KubeflowKubeflow
Weights & BiasesWeights & Biases
AWS SageMakerAWS SageMaker
GCP Vertex AIGCP Vertex AI

Model Serving

TensorFlow ServingTensorFlow Serving
TritonTriton
BentoMLBentoML
FastAPIFastAPI
custom inference APIscustom inference APIs

Data Storage

SnowflakeSnowflake
BigQueryBigQuery
RedshiftRedshift
Delta LakeDelta Lake
Apache IcebergApache Iceberg
PostgreSQLPostgreSQL

Infrastructure

DockerDocker
KubernetesKubernetes
TerraformTerraform
AWSAWS
GCPGCP
AzureAzure
Our Edge

We 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

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Social Proof

“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 Protocol
Engagement

How 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.

FAQ

Frequently Asked Questions

Data engineering focuses on building reliable pipelines that move, transform, and store data. MLOps focuses on deploying, monitoring, and maintaining ML models in production. Both are essential for production AI — and we build both.

Yes. Even one production model needs version control, monitoring, and a retraining strategy. Without MLOps, model performance silently degrades and debugging production issues becomes guesswork.

Yes. We assess your current pipelines, storage, and tools — then design and implement a migration path to modern data stack components (dbt, Airflow, Snowflake/BigQuery, etc.) with minimal disruption.

We implement data validation at every stage — schema checks, statistical profiling, freshness monitoring, and anomaly detection. Data quality issues are caught and flagged before they reach models or dashboards.

Blogs

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.

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