MLOps & AI Infrastructure

Move AI from experimentation to reliable production

Build the pipelines, platforms, deployment systems, and monitoring needed to run machine learning reliably, securely, and at scale.

FEATURED AI CLIENTS

Why machine learning models struggle to reach production

Fragile infrastructure blocks production readiness

Inconsistent environments, data pipelines, and dependencies make models difficult to reproduce, deploy, and scale.

Broken handoffs slow time to value

Disconnected workflows between data science, engineering, and operations delay deployment and create ownership gaps.

Model performance degrades over time

Without continuous monitoring and retraining, models decay over time and increase operational risk.

Poorly planned architectures increase cost

Uncontrolled compute, fragmented tools, and brittle infrastructure make AI systems expensive and difficult to maintain.

End-to-end MLOps and AI infrastructure services

MLOps & AI Infrastructure

MLOps strategy and readiness

Assess your current ML workflows, infrastructure, tools, and operating practices to identify production gaps. We define the target architecture, deployment model, technology stack, and implementation roadmap for cloud, on-premises, or hybrid environments.

MLOps & AI Infrastructure

ML platforms and automated pipelines

Build standardized platforms and automated pipelines for model training, validation, testing, and release. We implement experiment tracking, model registries, versioning, feature stores, and reproducible environments to improve consistency across the ML lifecycle.

MLOps & AI Infrastructure

Model deployment and serving

Deploy models reliably across batch, real-time, API-based, and containerized environments. We design scalable serving infrastructure with workload orchestration, autoscaling, controlled release strategies, and rollback mechanisms to reduce deployment risk.

MLOps & AI Infrastructure

Monitoring and model lifecycle management

Track model performance, data quality, drift, infrastructure health, and production failures from one connected monitoring layer. We implement automated alerts, retraining workflows, model lineage, and audit trails to maintain reliability over time.

MLOps & AI Infrastructure

AI infrastructure engineering

Design and implement secure, scalable infrastructure for training, deploying, and operating AI workloads. We manage compute and GPU requirements, infrastructure as code, production environments, availability, disaster recovery, access controls, and infrastructure costs.

Ready to operationalize your AI systems?

Assess your current MLOps setup and identify what is limiting deployment speed, reliability, and scale.

How we build production-ready MLOps systems

Assess the current environment

We review your ML workflows, infrastructure, tools, deployment practices, security controls, and operational bottlenecks.

Deliverables: MLOps maturity assessment | Current-state architecture map | Toolchain inventory | Production readiness gap analysis | Prioritized recommendations

Design the target MLOps architecture

We define how models, data, pipelines, environments, infrastructure, monitoring, and governance will work together.

Deliverables: Target architecture diagram | Deployment plan | Recommended MLOps toolchain | Environment and access-control design | Implementation roadmap

Build the platform and pipelines

We implement the core infrastructure and automate model training, testing, deployment, and release workflows.

Validate production readiness

We test the platform under realistic operating conditions to confirm performance, reliability, security, and recovery.

Operationalize and improve

We establish the processes, ownership, and controls needed to keep models reliable after deployment.

How we build production-ready MLOps systems

step 1

Assess the current environment

We review your ML workflows, infrastructure, tools, deployment practices, security controls, and operational bottlenecks.

Deliverables: MLOps maturity assessment | Current-state architecture map | Toolchain inventory | Production readiness gap analysis | Prioritized recommendations

step 2

Design the target MLOps architecture

We define how models, data, pipelines, environments, infrastructure, monitoring, and governance will work together.

Deliverables: Target architecture diagram | Deployment plan | Recommended MLOps toolchain | Environment and access-control design | Implementation roadmap

step 3

Build the platform and pipelines

We implement the core infrastructure and automate model training, testing, deployment, and release workflows.

step 4

Validate production readiness

We test the platform under realistic operating conditions to confirm performance, reliability, security, and recovery.

step 5

Operationalize and improve

We establish the processes, ownership, and controls needed to keep models reliable after deployment.

Turn ML investments into reliable production outcomes

Move models into production faster

Automated testing, validation, deployment, and release workflows reduce manual handoffs and shorten the path from experimentation to production.

Improve model reliability in production

Continuous monitoring, drift detection, and retraining keep models accurate, stable, and aligned with changing data and business conditions.

Reduce deployment and operational risk

Versioning, controlled releases, rollback mechanisms, and audit trails make model updates safer and easier to manage.

Lower AI infrastructure costs

Optimize compute, storage, and inference resources to reduce waste and improve visibility into the cost of running AI workloads.

Scale AI across more teams and use cases

Standardized platforms, reusable pipelines, and shared infrastructure make it easier to deploy more models without rebuilding the operating environment each time.

Build the systems needed to run AI reliably at scale.

Review Your AI Infrastructure

Case studies

45% improvement in model accuracy through real-time monitoring and automated retraining.

SaaS & TechnologyAutomated RetrainingDrift Detectionobservability

"Their monitoring system gave us complete visibility into model health. Drift alerts and retraining pipelines saved us from costly errors."

Director of Model Risk

2× faster experimentation using a centralized model registry and reproducible training environments.

SaaS & TechnologyMLflow setupOrchestrated Training PipelinesReproducibility

"tkxel helped us create a structured experimentation workflow. Our data scientists ship reliable models far more efficiently now."

Head of Data Science

58% reduction in cloud compute costs via infrastructure optimization and smart scaling.

SaaS & TechnologyCloud OptimizationKubernetes orchestrationResource Tuning

“Their cost optimization strategy helped us scale without overspending. We now run complex models at a fraction of the previous cost.”

VP Engineering

3× increase in model scalability after migrating to a Kubernetes-based multi-environment architecture.

SaaS & TechnologyHybrid MLOps SetupKubernetes orchestrationModel Serving

“Our infrastructure is now stable, scalable, and easy to manage. tkxel build an environment that supports real-time ML at global scale.”

Director of Platform Engineering

MLOps built for the realities of production

One team across the entire AI stack

Bring together machine learning, data engineering, cloud, DevOps, security, and application engineering without coordinating multiple specialist vendors.

Architecture that fits your existing environment

Build on your current cloud platforms, data systems, tools, and engineering workflows instead of forcing a costly rip-and-replace approach.

Production controls built in from the start

Design monitoring, security, access controls, model versioning, rollback, recovery, and cost visibility into the platform rather than adding them after deployment.

A platform your internal team can operate

Receive documented pipelines, runbooks, ownership models, training, and handover support so your team can manage and extend the environment after implementation.

Key technologies we work with

Cloud and managed ML platforms
Azure and Azure Machine Learning
Data and workflow orchestration
Containers and infrastructure automation
CI/CD and deployment automation
Monitoring and observability

AWS and SageMaker

Azure and Azure Machine Learning

GCP and Google Vertex

TensorFlow

PyTorch

Scikit Learn

Python Pandas

FastAPI

Apache Airflow

Kafka

Spark

Databricks

Docker

Kubernetes

Terraform

Ansible

Jenkins

GitHub Actions

GitLab CI

BitBucket Pipelines

CircleCI

ArgoCD

Prometheus

Grafana

DataDog

New Relic

Kibana

Download our MLOps readiness checklist

Assess data, model, and infrastructure maturity
Identify scalability and governance gaps
Benchmark your AI environment against best practices
Build a roadmap for reliable, compliant deploymenth

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Top 15 inspiring workplaces for 2026

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MLOps Services for Reliable Machine Learning Operations

MLOps becomes essential as models, teams, and production environments grow beyond what manual processes can handle.

Our MLOps services help automate model deployment, monitoring, infrastructure, and governance while keeping costs and risks under control.

We help teams build reliable MLOps foundations that support models from development through production and ongoing optimization.

Why Businesses Approve MLOps Investment Now

Budget owners didn’t always review machine learning spend line by line. Data science teams ran experiments, and the cost stayed inside a research budget. Three shifts changed that.

Model Count Outpaced Oversight

A single pilot model is easy to track by hand. Ten or twenty models running across different teams are not.

Spend on compute, storage, and engineer time started showing up without a matching view of which models were still earning their keep.

Failed Models Became Invisible Costs

A model that decays quietly still makes decisions. A pricing model, a fraud check, or a recommendation engine that drifts off target doesn’t fail loudly.

It costs money for months before anyone traces the loss back to a stale model.

Vendor Lock In Became a Budget Risk

A pipeline built entirely inside one cloud provider’s proprietary tools creates a dependency the business has to keep funding, even after a better or cheaper option exists.

A platform agnostic build protects that spend against future price changes.

MLOps Delivery Models Compared

MLOps engagements come with different pricing structures, and each one shifts risk to a different party. Picking the wrong one for the situation is a common, avoidable cost.

Delivery Model	How It Works	Who Carries the Risk	Best Fit
Time and Materials	Billed per hour or day of engineering work	Buyer	Ongoing, open-ended platform work
Retainer	Billed as a fixed monthly fee for a set capacity	Buyer	Steady-state monitoring and maintenance
Project-Based	Priced for a defined scope, billed in milestones	Shared	A platform build with a known end state
Outcome-Based (Fixed Cost)	Priced against a production readiness target, agreed before build starts	Vendor, until the target is met	A first implementation with clear success criteria

Outcome based pricing can work well for a first implementation, but only when production readiness can be defined and measured before the build starts.

If the criteria are vague, the pricing model won’t fix that. A project based or retainer structure is usually the safer starting point.

Who Needs to Be Involved in an MLOps Build?

The roles an MLOps build touches aren’t a surprise to anyone evaluating a vendor. What’s harder to plan is timing.

Some roles are needed on day one. Others can join later without slowing the project down. Confirm this during the assessment phase, not mid build.

Role	When and Why Involved
ML Engineers	Build phase, throughout
Data Engineers	Assessment and build phase
DevOps and Platform Engineers	Deployment and operation
Site Reliability Engineers	Production launch onward
Security and Compliance Specialists	Assessment onward, for regulated or sensitive use cases

If a use case needs a role not listed here, raise it during the assessment rather than discovering the gap mid build.

MLOps Governance: Who Is Accountable When the Model Is Wrong?

Automated pipelines move fast because they don’t wait on a person to approve every training run. That speed only holds up if accountability is assigned before something goes wrong, not decided in the moment.

Before the Model Goes Live

Who approves a model before it reaches production, and who covers that approval when they’re unavailable
Under outcome based pricing, the vendor may carry responsibility for meeting the agreed readiness criteria. Final business or regulatory approval should still be explicitly assigned in the contract

After the Model Goes Live

Who owns the model day to day: a technical owner accountable for how it runs, and a business owner accountable for whether it should keep running
Who reviews model behavior for fairness and bias, not just accuracy. That’s a different check than a drift alert, and it’s easy to leave unassigned
Who gets the incident escalation when a model fails in production, and how fast they’re expected to respond

MLOps Services vs. a Single ML Engineer

Hiring one ML engineer adds individual ML engineering capacity. An MLOps engagement adds the cross functional expertise needed to operationalize models across infrastructure, deployment, monitoring, and governance.

Need	ML Engineer	MLOps Engagement
Model Development	Strong	Strong
Training Pipelines	Partial	Full
Deployment Infrastructure	Depends on experience	Included
Monitoring and Drift Detection	Usually limited	Built into the platform
Governance and Sign-Off	Not typically owned	Can be assigned explicitly
Running Many Models at Scale	Limited	Designed for it

If the gap is model building capacity, one engineer can close it. If the gap is everything that keeps a model running once it’s live, that’s what an MLOps engagement is built for.

Ready to Elevate Military and Defense with AI?

Let’s explore how AI agents can help you move faster and smarter.

Build a strong foundation for scalable AI

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Global Director IT, Knowles

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COO, Sterne Kessler

“tkxel has proven beyond par that they excel not just in building and integrating with our team but building at a level that is at par with any US development team. Working with tkxel is one of the best decisions we have made.”

Umair Bashir

CTO, Replenium

“tkxel shared our vision right from the get go, and helped us achieve the unthinkable through perseverance and a thorough attention to detail. Their team was highly professional and possessed a firm grasp on technicalities, a combination that is hard to find in the industry.”

Pam Chitwood

Product Manager, ABB

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Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

Umair Bashir

CTO, Replenium

Pam Chitwood

Product Manager, ABB

Frequently asked questions

What is MLOps?

MLOps combines machine learning, data engineering, DevOps, and infrastructure practices to automate how models are trained, tested, deployed, monitored, and updated in production.

How is MLOps different from DevOps?

DevOps manages the delivery and operation of software applications. MLOps extends these practices to machine learning by adding data and model versioning, model validation, drift monitoring, retraining, and governance.

When does a business need MLOps?

MLOps becomes important when manual deployment slows releases, models behave inconsistently across environments, production performance is difficult to monitor, infrastructure costs increase, or multiple teams need to manage AI systems at scale.

Can tkxel improve an existing MLOps environment?

Yes. We assess your current architecture, pipelines, tools, monitoring, security controls, and operating practices, then modernize the areas causing deployment delays, reliability issues, unnecessary costs, or scalability limitations.

Can you work with our current cloud and technology stack?

Yes. We design MLOps environments around your existing cloud platforms, data systems, development tools, and engineering workflows. We support cloud, on-premises, and hybrid infrastructure without requiring a complete platform replacement.

How do you monitor models after deployment?

We implement monitoring for model performance, data quality, data and concept drift, latency, infrastructure health, failures, and resource usage. Alerts, dashboards, and retraining workflows are configured around your model and business requirements.

Does MLOps support generative AI and AI agents?

Yes. MLOps capabilities can support generative AI and agentic systems through deployment automation, infrastructure management, observability, versioning, evaluation, security, cost monitoring, and controlled releases.

How long does an MLOps implementation take?

The timeline depends on your existing infrastructure, number of models, integration complexity, security requirements, and required level of automation. The initial assessment defines the priorities, scope, and phased implementation plan.