CI/CD for Machine Learning
Automated testing, packaging and release of models and pipelines, so changes ship safely and repeatably.
AI Solutions · Automation & MLOps
AI that keeps running,
long after launch.
We automate the workflows around your models too: CI/CD for machine learning, monitoring, retraining pipelines and cost controls, so AI systems stay reliable and accountable.
Overview
Launch is where the real work begins.
A deployed model is not a finished product. Data drifts, user behavior changes, dependencies update and costs creep. Without monitoring and a way to retrain safely, model quality quietly degrades and nobody notices until it affects the business.
Our MLOps and automation engineers build the operational backbone that keeps AI dependable: automated build-and-release pipelines, observability, retraining workflows and governance. We also apply AI to automate the business processes surrounding your models, taking repetitive manual work out of the loop.
What's included
The operational layer AI needs.
Monitoring & Observability
Tracking of model quality, data drift, latency and errors, with alerts when something needs attention.
Retraining Pipelines
Automated, governed retraining on fresh data, with validation gates before anything reaches production.
Cost Controls
Visibility and optimization of compute, storage and API usage, so AI spending stays under control as usage grows.
Workflow & Process Automation
AI-assisted automation of manual, repetitive business processes such as intake, triage, reconciliation and reporting.
Governance & Auditability
Model versioning, lineage and documentation that make AI systems explainable and accountable to your stakeholders.
Where it fits
Who needs it.
Teams with models stuck in notebooks
Models degrading in production
Organizations scaling from one model to many
Regulated environments needing auditability
Back-office teams buried in manual work
AI budgets that are growing unpredictably
Why Tekleads
Reliable AI is an engineering discipline.
We bring DevOps rigor to machine learning, so your AI investments keep paying back instead of quietly decaying.
DevOps heritage
Pipelines, infrastructure-as-code and release discipline applied to machine learning workloads.
Visibility first
You see model health and spend in real time, rather than finding problems from user complaints.
Automation with oversight
Automated where it is safe, with human review where accountability requires it.
Technology
MLOps and automation tooling.
MLOps
Cloud AI
CI/CD & Infrastructure
How we work
How we operationalize AI.
Review the estate
We assess your current models, data flows, infrastructure and pain points.
Design the pipeline
A target architecture for release, monitoring and retraining that fits your platform and team.
Build & automate
Implement the pipelines, dashboards and automation, and validate them against real workloads.
Operate & improve
Hand over to your team, or continue to operate and tune the system with you.
FAQs
Common questions
What is MLOps, in simple terms?
MLOps applies DevOps practices to machine learning: automating how models are tested, released, monitored and retrained, so AI systems stay reliable over time.
Do we need MLOps if we only have one model?
Even a single production model benefits from monitoring and a safe retraining path. The level of tooling should match your scale, and we right-size it accordingly.
Can you automate business processes that don't involve a model we built?
Yes. Workflow automation is part of this service, using AI where it helps and conventional automation where it is simpler and cheaper.
Running AI in production?