Calgary, AB

DevOps for AI Systems in Calgary

MLOps that scales AI in production

Calgary is Canada's energy capital — home to the headquarters of Suncor, Canadian Natural Resources, Enbridge, and TC Energy — and a rapidly diversifying technology market where oil and gas digitisation, fintech, and agritech are building a post-oil knowledge economy.

100+
Projects Delivered
7+
Years Experience
50+
Expert Engineers
24/7
Support Available

DevOps for AI Systems for Calgary Businesses

Key Industries

Energy TechFinTechEnterprise SoftwareDigital Transformation

Tech Ecosystem

Companies in the area: Suncor, Canadian Natural Resources, Enbridge, ATB Financial, TC Energy

Service Overview

AI systems have unique operational requirements: model versioning, data pipelines, experiment tracking, and model monitoring. MLOps extends DevOps practices to handle these challenges.

We implement MLOps platforms and practices that enable your data science team to deploy models reliably and monitor them in production. Our solutions scale from single models to enterprise ML platforms.

MLOps is essential for turning experimental AI into production business value.

Why Devsdom?

SOC 2 & HIPAA Ready
Global Timezone Coverage
Agile Development Process
Dedicated Project Manager
Transparent Pricing
Post-Launch Support

Key Benefits

Why Calgary companies choose Devsdom for devops for ai systems

01

Model versioning and registry

02

Experiment tracking

03

Automated training pipelines

04

Model monitoring and alerting

05

A/B testing infrastructure

06

Feature stores

Common Use Cases

Production ML deployment

ML platform development

Model monitoring

Automated retraining

Experiment management

Success Stories

Autonomous Vehicle StartupPalo Alto, CA

MLOps Platform for Autonomous Vehicle Company

Challenge

An AV company was training hundreds of models but lacked infrastructure for versioning, deployment, and monitoring. Data scientists spent 40% of time on ops instead of research.

Solution

We built a complete MLOps platform with experiment tracking, model registry, automated training pipelines, A/B testing infrastructure, and comprehensive monitoring.

Outcome

Model deployment time reduced from weeks to hours. Data scientist productivity increased 60%. Now managing 200+ models in production with full lineage.

Hours vs weeks deploy
60% productivity gain
200+ models
Full reproducibility
5Engineers
8 months
PythonMLflowKubeflow

Our Process

A proven methodology for delivering successful projects

01
Discovery
Understanding your requirements
02
Planning
Architecture & roadmap
03
Development
Agile sprints & delivery
04
Testing
QA & security audits
05