CI/CD for AI
versioning, deployment, monitoring and retraining managed in one governed pipeline. As a CI/CD for AI services provider, QKTech automates training,
Taking the pain out of getting models into production
AI teams often face delays moving models from development to production because of manual steps, fragmented tools and compliance gaps. CI/CD for AI automates the path a model takes from training to production, with versioning, deployment, monitoring and retraining managed in one governed pipeline.
As a CI/CD for AI services provider, QKTech automates training, implementation, monitoring and version control, which keeps models traceable and governed, with built-in drift checks and retraining. Deployment stays consistent across cloud, on-premises and hybrid environments, through containerized pipelines built on tools such as MLflow, Kubeflow, GitHub and GitLab, with SageMaker or Azure ML for managed deployment.
Smooth model releases powered by governed workflows and dependable delivery
End-to-end ML pipelines
We bring code, data, models and configurations into one automated workflow, which removes the delays caused by fragmented tools and manual handoffs. Pre-built templates for major cloud platforms and modular components support stable rollouts and shorter release cycles.
Model versioning and governance
An integrated model registry records lineage, versions and change logs, which gives teams traceability, simple rollbacks, built-in compliance checks and audit trails.
Drift detection and continuous retraining
Drift detection is built into the pipeline with customizable thresholds, which means a loss of accuracy in production triggers an alert and retraining instead of going unnoticed.
Pick a feature or go full-suite
Unified control plane across cloud and on-prem
Containerized pipelines for portability
MLflow for tracking and registry
Kubeflow for training and serving
GitHub/GitLab for automated builds
SageMaker/Azure ML for managed deployment
Built-in security and compliance
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Recent pieces from our experts on AI delivery and reliability, and results from client engagements.
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Common questions about CI/CD for AI
What is CI/CD for AI?
CI/CD for AI is the practice of automating how machine learning models are built, tested, deployed and monitored, with versioning, governance and retraining built into one pipeline. QKTech delivers it across cloud, on-premises and hybrid environments.
How is CI/CD for AI different from CI/CD for software?
Software CI/CD ships code that behaves the same way until it changes, while a model also depends on data and can lose accuracy as that data shifts. CI/CD for AI therefore adds model versioning, drift detection and continuous retraining to the usual build and deploy stages.
How does QKTech handle model versioning and governance?
An integrated model registry records lineage, versions and change logs, which gives traceability, simple rollbacks, built-in compliance checks and audit trails.
How does drift detection work in the pipeline?
Drift detection is built into the pipeline with customizable thresholds. When accuracy falls in production, the pipeline raises an alert and triggers retraining.
Which tools does QKTech use for ML pipelines?
Pipelines are containerized for portability and use MLflow for tracking and registry, Kubeflow for training and serving, GitHub or GitLab for automated builds and SageMaker or Azure ML for managed deployment, all behind a unified control plane across cloud and on-premises.
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