DATA ENGINEERING & MLOPS

Build Scalable, Reliable and Governed Foundations for AI in Production

From experimental AI to enterprise-grade operations. This is not tooling for experimentation – this is production-grade AI infrastructure.

COMMON CHALLENGES

Why Data Engineering & MLOps Matter

Inconsistent Data Quality

Low-quality or inconsistent data leads to unreliable model outputs and broken analytics.

Manual ML Deployments

Manual, fragile deployment processes cause delays, errors, and inconsistency across environments.

Models Failing Silently

Without proper monitoring, model drift and degradation go undetected until business impact occurs.

Lack of Monitoring

No visibility into pipeline health, model performance, or data quality across the ML lifecycle.

Difficulty Scaling

Cannot scale AI across teams and use cases without repeatable, automated foundations.

Poor Governance

Lack of audit trails, version control, and compliance frameworks creates risk and liability.

CORE CAPABILITIES

What Data Engineering & MLOps Enable

Data Engineering Foundations

Scalable data pipelines (batch & real-time)
Data ingestion from internal/external sources
Transformation, validation & quality checks
Feature engineering & feature stores
Data warehouses, lakes & lakehouses

MLOps & Model Operations

Platform & Infrastructure

Ready to turn AI from projects into platforms?

IDEAL FOR

Who This Service Is For

Scaling Beyond Projects

Organisations wanting to scale AI beyond isolated projects into repeatable, enterprise-wide capabilities.

Production-Grade Operations

Companies needing production-grade AI operations with strong governance, compliance, and reliability.

Multiple AI Models

Businesses operating multiple AI models and use cases that require unified operations and monitoring.

OUR METHODOLOGY

Our 5-Step Approach

Data & AI Platform Assessment

Assess current data sources, pipelines, ML workflows, infrastructure, and operational maturity. Identify bottlenecks, security gaps, and governance needs.

Architecture & Platform Design

Design end-to-end architectures covering data ingestion, feature engineering, ML training/inference pipelines, CI/CD workflows, and observability.

Implementation & Automation

Build data pipelines, automated ML deployments, model registries, and monitoring frameworks. Replace manual work with repeatable automation.

Governance, Security & Compliance

Implement GDPR-aligned data handling, role-based access, audit logs, controlled releases, and documentation. Governance enables safe AI at scale.

Optimisation & Continuous Improvement

Pipeline performance optimisation, cost monitoring, continuous model evaluation, and platform evolution. AI platforms must evolve continuously.

WHAT YOU RECEIVE

Deliverables

Assessment Report

Platform Architecture

Scalable Pipelines

ML CI/CD Pipelines

Drift Detection

Governance Framework

Full Documentation

Production Foundations

Ready to Build Production-Grade AI Foundations?

Talk to Veritaz – your partner for Data Engineering & MLOps in Sweden and the EU.