Certified Mendix partner
MLOps Services
MXTechies provides MLOps services that take your machine learning models from notebook to production and keep them accurate. We automate deployment, monitoring and retraining, and connect models to real business apps using Mendix low-code.
North AmericaEuropeAsiaMiddle East
PipelineCI/CD for ML
ServingSafe rollout, easy rollback
DriftAlerts before users notice
RegistryEvery model versioned
First model live in about 6 to 8 weeksthen scale
Quick answer
What are MLOps services?
MLOps services set up the tools and processes to deploy, monitor and update machine learning (ML) models in production. MXTechies, a certified Mendix partner, builds automated pipelines, tracks model quality and drift, and connects models to business apps, so your models stay reliable, secure and easy to manage. A first model typically goes live in about 6 to 8 weeks.
Warning signs
Signs You Need MLOps
Each deployment takes weeks or months.
No one is sure which model version is live.
Accuracy drops and nobody notices until users complain.
Auditors ask for model records you cannot provide.
Cloud and GPU costs keep rising with no clear reason.
Services
Our MLOps Services
01
ML pipelines (CI/CD)
Automate training, testing and release of models.
02
Deployment and serving
Safe rollout, easy rollback and fast model APIs.
03
Monitoring and drift alerts
Catch accuracy drops before users do.
04
Model registry and governance
Version every model and dataset, with approvals and audit trails.
05
LLMOps for GenAI
Test quality, safety and cost for large language models.
06
Run and cost control
Support, updates and tracking of inference and cloud spend.
Explainer
MLOps vs DevOps: What Is the Difference?
| DevOps | MLOps | |
|---|---|---|
| What is managed | Code | Code, data and models |
| Testing | App tests | App tests plus accuracy and bias tests |
| After release | Uptime and errors | Uptime plus drift and accuracy decay |
| Updates | New code release | Retraining on new data |
Why MXTechies
Why Choose MXTechies for MLOps
Works with your stack
We add to your tools, we do not force a rebuild.
Models to apps
With Mendix, model results reach users through real apps, not just APIs.
Real GenAI experience
We built GenAI PIAM, with AI suggestions, image recognition and translation.
Mendix specialists
Reseller, expert services, training, ISV and support in one partner.
You own it
We train your team and hand over full documentation.
Secure and Governed ML Models
ML models make decisions, so they need control and records. We use access control, audit trails, model cards and approval steps for every model. We design to support governance needs such as the EU AI Act, GDPR and HIPAA, and model risk guidance in banking such as SR 11-7. Your compliance team keeps final sign-off.
Further reading: Mendix partner profile · Google Cloud MLOps guide · MLflow documentation · NIST AI RMF
Industries
Industries We Serve
Manufacturing
Shop floor, quality, MES, and operational applications
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Retail Digital Solutions
Inventory, order management, and customer portals
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Business Services
Workflow automation and internal enterprise tools
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Financial Services
Secure process automation and reporting applications
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Insurance
Claims, policy, and compliance workflows
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Logistics & Transportation
Tracking, planning, and operational visibility
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EV Solutions
EV operations, manufacturing, and analytics
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Electronics
Traceability, quality, and production apps
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Our 5-Step Process
- 1
Assess
Review your current ML setup and find quick wins.
- 2
Build the foundation
Set up pipelines, registry, testing and monitoring.
- 3
Launch the first model
Move one model to production with full records.
- 4
Scale
Add more models and connect them to your apps.
- 5
Optimize
Review drift, quality and cost on a regular schedule.
FAQ
Frequently Asked Questions
What is MLOps?
MLOps (machine learning operations) is the set of tools and practices that deploy, monitor and update machine learning models in production. It keeps models accurate, secure and easy to manage after launch.
How is MLOps different from DevOps?
DevOps manages code. MLOps also manages data and models: it tests model accuracy, tracks which data trained each model, watches for drift after release and retrains when quality drops.
Do we need to replace our current tools?
No. We build on your existing cloud and tools (for example MLflow, SageMaker, Vertex AI, Azure ML or Databricks) and fill the gaps. We govern and automate what you already have.
How do you handle model drift?
We monitor input data, model outputs and accuracy all the time. When quality drops past a set limit, the system alerts your team, starts a retraining check or switches to a safe fallback model.
Can you support LLMs and GenAI (LLMOps)?
Yes. We apply the same discipline to large language models: quality testing, safety checks, cost tracking and version control for prompts and models.
How long does it take to get the first model into production?
A first model usually reaches production in about 6 to 8 weeks, depending on your data, tools and approvals. We start with one model, prove the process, then scale.
How much do MLOps services cost?
Cost depends on the number of models, tools and compliance needs. We start with a fixed-scope assessment or pilot, then share a clear estimate before you scale.
Next step
Put Your ML Models to Work, and Keep Them Working
Share your goal. We will review your ML setup and suggest the best first model, a clear timeline and an honest view of cost.