Machine Learning Operations (MLOps) Services
Many models silently lose accuracy after launch, with no drift visibility and slow manual redeploys. Machine Learning Operations (MLOps) is the discipline that keeps deployed models accurate over time. Kodexo Labs delivers CI/CD pipelines, live monitoring, and governed retraining to fix that gap.
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TRUSTED BY ENTERPRISES
We work with teams that already run a model in production, not those starting from scratch. If your model shipped months ago and its accuracy is quietly slipping, this page is for you.
Our Core Capabilities
Ship models to production through automated CI/CD pipelines, safely.
Track live accuracy and catch model drift before users do.
Version every model and roll back to known-good releases.
Retrain automatically on drift, orchestrated by LangGraph pipelines behind deployment.
Version training data and serve features from one store.
Govern access, detect bias, and keep a full audit trail.
IN THE NEWS
AI-powered products across 25+ industries
Earned Top-Rated Reviews on Clutch
Client retention across long-term engagements
In 2021 and headquartered in Austin, Tx
MLOps Capabilities Built for Production Scale
These are the MLOps modules we run start to finish, from your first production deploy through ongoing retraining. Each capability below covers one part of keeping a live model accurate, monitored, and governed as data shifts.
Deployment & CI/CD
Manual model rollouts stall releases. For Diesel Laptops, we deployed inside their private AWS VPC, meaning their data never left their own environment.
We test each change, then ship the new model live with no manual steps.
We pack each model in Docker, then roll it out in safe, staged steps.
Ready to Deploy Machine Learning Models with Confidence?
Catch drift before it quietly costs you accuracy. Replace manual redeploys with CI/CD pipelines, and keep every production model dependable long after launch.
Roadmaps That Shipped Results
Diesel Laptops
Fleet technicians lost minutes on every job hunting through 160,000 parts records. Solution: Kodexo Labs built an AI parts-lookup running inside their own self-hosted AWS VPC, keeping all data on their private cloud. Outcome: lookup time fell 85%, so a technician finds the right part in seconds, not minutes, on every daily job.
85%
Faster Lookup
160,000
Records Searched
Inc. 5000
Client
Extensiv
This $130M-funded logistics platform had operations staff waiting on engineers for every data question spread across 4 databases. Solution: Kodexo Labs built a LangGraph agentic system that reads plain-English questions and answers them directly. Outcome: staff now query 207 tables themselves, at over 90% accuracy, with answers arriving in seconds instead of days.
90%+
Accuracy
207
Tables
$130M+
Funded (Hg Capital)
SmartMedHx
Clinicians were losing visit time to manual note-taking during patient appointments. Solution: Kodexo Labs built HIPAA-compliant, patent-pending documentation AI that captures the conversation and writes the notes automatically, keeping patient data protected. Outcome: 42 providers now document visits hands-free, 493 patient interviews processed, and the client returned for a second, separate legal-tech build.
42
Providers
493
Interviews Processed
HIPAA
Compliant From Day One
Ping Force
Ping Force needed a reliable IT infrastructure monitoring product, built exactly to spec and delivered on committed timelines. We scoped the build with their team, then shipped each milestone on schedule with no scope drift. Brian Musgrave saw every deadline met, every requirement delivered, and every category verified through Clutch across the whole engagement.
On-Time
Milestone Delivery
Zero
Scope Drift
5
Alert channels
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
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How Machine Learning Operations Serves Every Industry We Support
The operational discipline stays the same across every sector we serve. What shifts is compliance rules, data-residency limits, and retraining triggers each vertical demands. We adapt deployment and governance to those requirements without rebuilding our core approach.
- HIPAA-compliant model deploymentClinical model monitoringAudit-ready retraining pipelinesPHI-safe self-hosted inference
Still Watching Your Model Accuracy Slip After Every Single Deployment?
Drift is quiet until it costs you a customer. If retraining keeps slipping down your team's list, we can take that weight off. Let's map a monitoring and retraining plan that actually holds.
Why Enterprises Trust Kodexo Labs to Operate Their Machine Learning Systems at Scale
Most models break after launch, not before.
Accuracy drifts, retraining stalls, and nobody owns the pipeline. We stay accountable for the parts that keep production systems reliable after the demo impresses everyone in the room.
MLOps Owned Through Production
For Extensiv, we built an agentic pipeline that reads 207 tables in 4 databases and now runs live at 90%+ accuracy. We used LangGraph, then owned the build, the launch, and daily ops.
Bridging Notebook to Production
Syed Umaid Ahmed is a PhD Scholar at FAST-NUCES, Lead ML Engineer, Computer Vision specialist, and Microsoft Certified BI Analyst. He builds LangGraph flows that hold up in real use, not a demo.
Self-Hosted, Data Stays Yours
Diesel Laptops runs in a self-hosted AWS VPC only they control, across 160,000+ records. For SmartMedHx, we kept each call HIPAA-compliant on hardware the client alone owns, never on an API we host.
Your On-Demand MLOps Bench
Hiring a full MLOps team is out of reach for most mid-market companies. We give you the function on demand. 94% of clients stay, and we have shipped 51 products across 25+ industries.
Not Sure Where Your ML Pipeline Breaks?
Maybe drift is quietly eating your accuracy. Maybe deployments stall for weeks, retraining jobs fail silently, or governance gaps worry your auditors. We'll walk your production pipeline with you and pinpoint exactly where it's breaking down.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
Overcoming Common Machine Learning Operations Challenges
Most machine learning projects don't fail during model-building. They fail after launch, when accuracy drifts, pipelines break, and nobody notices until results suffer. The 4 problems below cause the majority of production ML failures we see across client engagements today.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
Our Proven Five-Phase Machine Learning Operations Process
Our roadmap is agile and iterative. We start by auditing what you've already deployed, not by building new models from scratch.
Discovery & Pipeline Audit
We assess your existing models, data pipelines, and deployment gaps before touching a single line of code. This audit tells us what's stable, what's silently failing, and where the fastest wins actually are.
Model Deployment Architecture
Next, we design a CI/CD-ready deployment architecture for your target environment, whether that's a public cloud or a self-hosted setup inside your own network. The design accounts for security, latency, and how your team already works.
CI/CD & Automation Build
This phase delivers the automated deployment, versioning, and registry pipelines that move models to production reliably. We orchestrate multi-step workflows in LangGraph, a framework for chaining and coordinating model tasks, so retraining and promotion run without manual steps.
Monitoring & Governance Setup
We stand up drift and accuracy monitoring, audit trails, and role-based access controls. From this point, you can see when a model starts slipping, prove who changed what, and trust that nothing ships unreviewed.
Handoff & Continuous Optimization
Finally, we transition to your team or stay on for ongoing retraining support. If you'd rather build in-house capacity, you can hire an MLOps engineer through our staff augmentation option and keep the momentum going.
Related Insights
What is MLOps?
January 2024 · By Kodexo Labs
What MLOps is and how it works, covering MLOps vs DevOps, key components, tools, platforms, the MLOps engineer role, and leading MLOps companies.
What is Machine Learning (ML)?
May 2025 · By Kodexo Labs
What machine learning is and how it relates to AI and deep learning, covering key concepts, types of ML, how models are built, and real-world applications.
Kodexo Labs – Crowned the Top Machine Learning Company 2024, by Clutch!
March 2024 · By Kodexo Labs
Kodexo Labs was named the Top Machine Learning Company in 2024 by Clutch, a leading B2B ratings platform, recognizing its AI, ML, and custom software expertise.
Common MLOps Questions
Machine learning operations, or MLOps, is the discipline of deploying, monitoring, and maintaining machine learning models in live production. It borrows CI/CD practices from software engineering and adds model-specific concerns like drift detection and continuous monitoring. Kodexo Labs, founded in 2021, treats MLOps as everything that keeps a trained model accurate and reliable after it launches, not just the initial build.