Deploy, Monitor & Scale Machine Learning Models with Confidence
bCubex designs and automates end‑to‑end MLOps pipelines for training, deployment, and monitoring ML models in production. We make your ML workflow repeatable, auditable, and production‑ready in 30 hours – $2,500.
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The Challenge
Machine learning models often work in notebooks but fail in production. Teams struggle with:
- Manual, inconsistent model deployment
- No version control for datasets and models
- Lack of monitoring and rollback when accuracy drifts
- Slow iteration from training to production
Our Solution
We build a CI/CD pipeline for ML workflows that handles everything from data ingestion to model monitoring, ensuring reproducible and scalable deployments.
Step 1: Automate Data & Model Versioning
- Track datasets and trained models in version control
- Automate retraining pipelines when new data arrives
- Store and tag artifacts for reproducible experiments
Tools: MLflow, DVC, S3, GitHub Actions, Amazon SageMaker
Step 2: Build & Deploy Models as Services
- Containerize models for consistent execution
- Automate testing and validation of models before release
- Deploy models to staging and production environments via CI/CD
Tools: Docker, Kubernetes, SageMaker, TensorFlow Serving, TorchServe
Step 3: Monitor & Retrain Automatically
- Implement live performance monitoring and drift detection
- Trigger automated retraining workflows on accuracy drop
- Maintain audit logs and rollback to previous versions instantly
Tools: Prometheus, Grafana, SageMaker Pipelines, Kubeflow
The Outcome
With bCubex’s automated MLOps pipeline, your ML models move seamlessly from research to production with full versioning, monitoring, and retraining automation. Deployments are consistent, auditable, and scalable, enabling you to iterate faster while maintaining accuracy and compliance.
Case Study: Cloud Consulting for Construction Company
Cubex Systems Pty Ltd has successfully enhanced the client's backend, meeting their expectations. Their effective communication and responsiveness have fostered a positive working relationship. In addition to their technical expertise, their exceptional customer service is a hallmark of their work.
