WORKFLOX Services
Your Model Works. Your Deployment Doesn't.
Production MLOps Pipelines That Don't Break
A model in a notebook is a prototype, not a product. We build the CI/CD, monitoring, and retraining infrastructure that turns a model your data science team trained once into a system that keeps working reliably, month after month.
The Challenge
Why ML Models Fail in Production
- ✗The model works great in the notebook, then nobody deploys it
- ✗No monitoring, so accuracy silently degrades as real-world data drifts
- ✗Retraining is a manual process someone has to remember to run
- ✗No versioning. Nobody can say which model version is actually live
- ✗A single engineer holds all the deployment knowledge in their head
Our Approach
What We Build
- ✓CI/CD pipelines that deploy model updates without manual intervention
- ✓Automated drift detection that flags accuracy degradation before it costs you
- ✓Model versioning and rollback so you can always trace and revert what's live
- ✓Scheduled or trigger-based retraining pipelines using fresh production data
- ✓Dashboards giving your team visibility into live model performance
Use Cases
What We Build For You
Model Deployment Pipeline Build
We containerize your trained model and build the CI/CD pipeline to deploy it as a scalable API endpoint.
Drift Detection & Alerting
Automated monitoring that compares live prediction distributions against training data and alerts your team when a model needs retraining.
Automated Retraining Pipelines
Scheduled or triggered pipelines that retrain models on new production data and run automated validation before promoting to production.
Model Versioning & Experiment Tracking
A structured registry tracking every model version, its training data, and its performance metrics for full auditability.
Legacy Notebook-to-Production Migration
Taking a data science team's working notebook model and rebuilding it as a properly tested, deployable production service.
Multi-Model Serving Infrastructure
Infrastructure to serve multiple models simultaneously with A/B testing and gradual rollout capability.
Our Process
Step-by-Step Development Process
01
Current State Assessment
We audit your existing model, its training process, and how (or whether) it's currently deployed.
02
Pipeline & Infrastructure Design
We design the CI/CD, serving, and monitoring architecture suited to your model's scale and update frequency.
03
Deployment & Monitoring Build
We build the automated deployment pipeline along with drift detection and performance dashboards.
04
Retraining Automation
We implement scheduled or triggered retraining with automated validation before any new version goes live.
05
Handoff & Documentation
We document the full pipeline and train your team to operate and extend it independently.
Technology
Our Stack
A typical mlops consulting services project here uses MLflow, Kubeflow, Docker and Kubernetes. We confirm the final choices with you at scoping, based on the systems you already run and the constraints you have.
FAQ
Frequently Asked Questions
We already have a trained model. Do we need MLOps if it's just running on one server?
If your model is running on a single server with no monitoring or versioning, you're one bad deploy or one silent accuracy drop away from a production incident nobody notices until customers complain. MLOps isn't about scale, it's about not flying blind.
What is model drift and why does it matter?
Drift is when the real-world data your model sees in production diverges from the data it was trained on, causing accuracy to degrade over time. Without drift detection, this happens silently. The model keeps making predictions, they just get progressively less accurate.
Can you set up MLOps for a model you didn't build?
Yes. A large share of our MLOps engagements start with a model built by an in-house data science team or another vendor. We assess the existing model and build the deployment, monitoring, and retraining infrastructure around it.
How often should models be retrained?
It depends entirely on how fast your underlying data changes. Some fraud models benefit from weekly retraining, while a stable forecasting model might only need quarterly updates. We set retraining cadence based on measured drift, not an arbitrary schedule.
How much does an MLOps engagement cost?
A focused deployment pipeline for a single model with basic monitoring typically costs $4,750–$10,000. Full MLOps infrastructure with automated retraining, drift detection, and multi-model serving ranges $12,000–$27,000.
MLOps Consulting for Production ML Teams in USA, UAE & Saudi Arabia
WORKFLOX builds the operational backbone that gets machine learning models out of notebooks and into reliable production systems. We work with in-house data science teams across the GCC and US to add the CI/CD, monitoring, and retraining automation their models were never built with.
Model Governance for Regulated Industries
For fintech and healthcare clients operating under regulatory scrutiny, we build model versioning and audit trails that document exactly which model version made which decision and when. A requirement increasingly enforced by regulators reviewing automated decisioning systems in the GCC and beyond.
Industries We Serve
Industry-Specific Expertise
Next step
Scope Your MLOps Consulting Services Project
Tell us what you are building and which systems it touches. We will reply with a recommended approach and a fixed-price proposal for your mlops consulting services project, with no commitment.
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