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MLOps & Model Lifecycle Management

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  • Reach a Higher ROI
    Eliminates manual bottlenecks and reduce deployment risk to shorten time-to-market, reduce operational costs, and maximize the return on every AI initiative

  • Boost Your Data Management
    Transform fragmented datasets for faster model development, better collaboration between data scientists and engineers, and more reliable production outcomes

  • Minimize Manual Overhead
    Replace repetitive manual processes with automation for higher productivity, fewer human errors, faster iteration cycles, and a scalable framework

Why It Matters

Automate tasks and deploy models quickly, ensuring data scientists, engineers, and IT cooperate smoothly and improve models for utmost accuracy and performance

Machine learning models create value only when they reliably reach production and continue performing after deployment. Without MLOps, companies often struggle with fragmented workflows, manual deployments, inconsistent environments, and models that degrade over time without visibility. Here’s why companies are choosing MLOps services:

  • Faster Time to Market. Accelerate the journey from experiments to business value. Reduce release bottlenecks and enable teams to deliver machine learning capabilities faster and with greater confidence.
  • Scalability. Support growing datasets with automated infrastructure provisioning, orchestration, and workload management designed for enterprise-scale AI initiatives.
  • Reproducibility and Traceability. Track datasets, features, experiments, model versions, and deployment history across the entire machine learning lifecycle for every model so that you can reproduce, audit, and validate it.
  • Stronger Collaboration. Create a shared operating model for data scientists, machine learning engineers, software developers, DevOps teams, and IT stakeholders.
  • Continuous Model Performance Optimization. Monitor accuracy, latency, drift, data quality, and business KPIs in production easily through continuous reports. automated alerts and retraining workflows.
  • Better Governance, Security, and Compliance. Establish approval workflows, audit logs, explainability frameworks, and access controls that support requirements.
  • Reliable Production Deployments. Reduce deployment risk through automated testing, CI/CD pipelines, promotion gates, rollback mechanisms, and controlled release strategies for the improved the stability and reliability.

Modernizing unstable systems? Launching new products?

We build development environments that deliver enterprise-grade scalability, compliance-driven security, and control baked in from day one.

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Why choose Devox Software

We Tackle the MLOps Services Business Challenges

  • Modernize
  • Build
  • Innovate

Production version was last copied to storage?

We set up a model registry with versioning, lineage, and a tested rollback path, so every deployed model can be traced, audited, reproduced, and restored within minutes if issues arise.

Retraining takes weeks and one engineer?

We implement scheduled and trigger-based retraining with regression evaluation gates and automated rollback, allowing model updates to occur in days instead of weeks.

Cannot reproduce a model from two months ago?

Data versioning, experiment tracking, and reproducible training pipelines are built into the foundation, so every model can be recreated, audited, and validated from a known state.

CI/CD exists for code but not for models?

We embed promotion gates, automated validation, A/B testing, shadow deployments, blue-green rollout strategies, and multi-cloud model serving so machine learning releases become as reliable, governed, and repeatable as software deployments.

Starting from zero with no platform team?

We, as an MLOps engineering firm, deliver production-ready MLOps platforms, with model registry, CI/CD pipelines, feature store, monitoring, and governance controls, so your first model can move from experimentation to production.

Feature engineering duplicated across teams?

A centralized feature store creates a single source of truth for features across training and inference environments, so teams eliminate duplication and ensure models use consistent feature definitions.

Drift discovered by customers, not monitors?

Solid statistical drift detection across model inputs, outputs, and prediction quality, with automated alerting, makes performance degradation controllable and addressed before it impacts users or business operations.

Compliance review blocks every release?

We offer MLOps managed services with audit logs, model cards, lineage tracking, approval workflows, and NIST AI RMF/ISO 42001-aligned governance controls. So risk, security, and compliance teams can review and approve model releases without creating deployment bottlenecks.

Inference cost growing faster than the value?

We optimize serving infrastructure through latency tuning, model tiering, autoscaling policies, workload right-sizing, and FinOps monitoring, so businesses control operational costs and maintaining performance, reliability, and service-level objectives on the go.

What We Offer

MLOps Services We Provide

  • Training Pipelines and Feature Stores

    Eliminate manual experimentation and inconsistent model behavior with training pipelines, feature engineering workflows, data versioning systems, and experiment tracking frameworks so that you can reproduce, audit, and validate it any time you need.

    We introduce standardized MLOps workflows that create a single source of truth for data, features, and model development. What exactly you get:

    • Automated and reproducible training pipelines
    • Data, feature, and code versioning
    • Centralized feature store shared across training and inference
    • Experiment tracking and model comparison
    • Automated feature engineering workflows
    • Dataset lineage and reproducibility controls
    • Training orchestration and scheduling
    • Audit-ready model development process

    As a result of these MLOps services, teams dramatically reduce the time required to retrain, validate, and deploy new models. at that the quality only improves.

  • Model Registry, CI/CD, and Deployment

    Gain a structured release process with governance, rollback mechanisms, and deployment observability built into every stage. More of what you get with MLOps implementation services:

    • Centralized model registry with versioning and lineage
    • CI/CD pipelines for machine learning workflows
    • Automated testing and promotion gates
    • Model approval workflows and governance controls
    • A/B, canary, shadow, and blue-green deployments
    • Multi-cloud and hybrid deployment support
    • Rollback and recovery mechanisms
    • Deployment monitoring and release tracking

    As a result, you ship faster from proof-of-concept to production and with lower risks.

  • Model Monitoring, Drift Detection, and Retraining

    Get continuous monitoring systems that track model health, detect drift, identify quality issues, and trigger retraining workflows before performance degradation impacts business operations. This way, the models remain accurate, reliable, and aligned with current business realities long after initial deployment with MLOps development services. The deliverables include:

    • Accuracy, precision, recall, and latency monitoring
    • Data drift detection
    • Fairness and bias monitoring
    • Automated alerting and incident integration
    • Scheduled and trigger-based retraining workflows
    • Regression testing and quality gates
    • Automated rollback protection

    As a result, you maintain model quality over time, reduce operational risk, and ensure machine learning systems continue delivering measurable business value.

  • MLOps Governance and Compliance

    Establish the controls, transparency, and accountability required to operate machine learning systems in enterprise environments. Get governance frameworks that give you complete visibility into how you train, approve, deploy, and monitor models throughout their lifecycle: which model version was deployed, which data was used, who approved the release, and how decisions can be audited. What you get with MLOps implementation services:

    • Complete model and data lineage tracking
    • Audit logs and approval workflows
    • Model cards and documentation standards
    • Role-based access controls
    • Regulatory compliance mapping
    • NIST AI RMF and ISO/IEC 42001 alignment
    • GDPR, HIPAA, SOC 2, and industry-specific controls

    As a result, you accelerate AI adoption while maintaining regulatory compliance and reducing audit risk, 3 in 1.

Our Process

How We Work

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01.

01. Assessment & Analysis (1–3 weeks)

We begin MLOps services by auditing your current state across data, training, registry, deployment, monitoring, and governance to craft a strategy for further activities. You get a written maturity score from 0 to 5 per dimension and a gap-priority list before any platform work begins.

02.

02. Architecture & Roadmap (2–3 weeks)

We design a target-state reference architecture for your cloud and tools, then sequence it into a budgeted 12-month roadmap. MLOps implementation services include deliverables in the form of an architecture document and a roadmap with budget bands.

03.

03. Build & Development (4–8 weeks)

We set up the feature store, model registry, CI/CD, and baseline monitoring, then wire one production model through the platform end-to-end. The output of this phase is a working machine learning lifecycle that your team can use as a blueprint for future models, reducing time-to-production and operational risk.

04.

04. Production Rollout (3–6 weeks)

We migrate the in-scope model portfolio onto the platform and configure rollback, A/B testing, and shadow deployments. As a result of this MLOps implementation services phase, you gain a production-ready machine learning operating model that reduces deployment risk, accelerates release cycles, and improves long-term model reliability.

05.

05. Operations Enablement (2–4 weeks)

During this phase, we provide hands-on training, document operational procedures, establish ownership models, and define escalation paths for production incidents, model degradation, and retraining events. As a result, you receive a complete operations playbook, documented runbooks, and a structured handover to internal teams.

06.

06. Operate & Improve (Ongoing)

Our team continuously monitors model performance, data quality, deployment health, operational costs, and platform reliability while helping your machine learning ecosystem scale alongside business growth. So you can be sure that your MLOps platform is supported to keep models accurate and compliant as the portfolio expands.

  • 01. Assessment & Analysis (1–3 weeks)

  • 02. Architecture & Roadmap (2–3 weeks)

  • 03. Build & Development (4–8 weeks)

  • 04. Production Rollout (3–6 weeks)

  • 05. Operations Enablement (2–4 weeks)

  • 06. Operate & Improve (Ongoing)

Benefits

Value We Provide

01

Quality Excellence

Our internal quality centers, including the Project Management Office (PMO), Business Analysis Office (BAO), and Quality Management Office (QMO), provide independent oversight throughout scope, timelines, budget, risks, and quality standards, ensuring every engagement progresses predictably and aligns with business objectives.

02

Lower Time-to-Market

Through automated testing, CI/CD pipelines, infrastructure-as-code practices, reusable architectural patterns, and our proprietary AI Solution AccelratorTM framework, we significantly reduce up to 70% the time required to move models from experimentation into production.

03

Vendor-Agnostic, No Lock-In

Our MLOps services are based on your preferred cloud providers, tools, contracts, and operational model. Whether the best fit is open-source technologies or managed platforms, the decision is driven solely by technical and economic considerations, with source code and assets remaining under your exclusive control.

04

Production-Grade by Default

Every model we ship has observability, an evaluation harness, a rollback path, and a documented retraining trigger to comply with the standards. The demo-to-production gap is closed before launch, not patched after the first incident, and drift is caught by a monitor rather than a customer.

Tech Stack

MLOps Services Technologies We Use

01

MLOps Platforms

  • AWS SageMaker – End-to-end ML development and deployment
  • Azure ML – Scalable cloud-based ML workflow management
  • Google Vertex AI – Unified ML platform for enterprise applications
  • Databricks MLflow – Open-source platform for ML lifecycle tracking
02

Model Monitoring Tools

  • Arize AI – Real-time model observability and drift detection
  • Fiddler AI – AI fairness, bias detection, and explainability
  • Evidently AI – Open-source tool for monitoring ML performance
03

Automation & Orchestration

  • Kubeflow – Kubernetes-native ML pipeline orchestration
  • MLflow – ML lifecycle management with experiment tracking
  • TensorFlow Extended (TFX) – End-to-end ML pipeline automation
MLOps Platforms Model Monitoring Tools Automation & Orchestration
01

MLOps Platforms

  • AWS SageMaker – End-to-end ML development and deployment
  • Azure ML – Scalable cloud-based ML workflow management
  • Google Vertex AI – Unified ML platform for enterprise applications
  • Databricks MLflow – Open-source platform for ML lifecycle tracking
02

Model Monitoring Tools

  • Arize AI – Real-time model observability and drift detection
  • Fiddler AI – AI fairness, bias detection, and explainability
  • Evidently AI – Open-source tool for monitoring ML performance
03

Automation & Orchestration

  • Kubeflow – Kubernetes-native ML pipeline orchestration
  • MLflow – ML lifecycle management with experiment tracking
  • TensorFlow Extended (TFX) – End-to-end ML pipeline automation
Case Studies

Our Latest Works

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Searchable Archive IA Engine for a European Legal Tech Provider Searchable Archive IA Engine for a European Legal Tech Provider

Searchable Archive IA Engine for a European Legal Tech Provider

An on-prem intelligent automation engine that transforms legacy legal scans into a searchable, audit-grade archive with sub-second full-text discovery and language-adaptive OCR.

Additional Info

Core Tech:
  • FastAPI
  • .NET 6 wrapper
  • React 18
  • Elasticsearch 8.x
  • PostgreSQL
  • Docker Swarm
  • Tesseract
  • Grafana
  • GitLab CI/CD
  • PDF/A-3b sealing
Country:

Germany Germany

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Function4 Function4
  • website
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Function4: Event Management Platform for the Financial Services Industry

A feature-rich system for managing tickets, devices, invites, and communication at scale.

Additional Info

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  • Vue js
  • GSAP
  • Ruby
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Country:

USA USA

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Real Estate App: Performance, Transparency, and Client Success in One Platform Real Estate App: Performance, Transparency, and Client Success in One Platform

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Additional Info

Core Tech:
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Testimonials

Testimonials

Carl-Fredrik Linné Carl-Fredrik Linné
Carl-Fredrik Linné Sweden

The solutions they’re providing is helping our business run more smoothly. We’ve been able to make quick developments with them, meeting our product vision within the timeline we set up. Listen to them because they can give strong advice about how to build good products.

Carl-Fredrik Linné
Tech Lead at CURE Media
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Darrin Lipscomb Darrin Lipscomb
Darrin Lipscomb United States

We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.

Darrin Lipscomb
CEO, Founder at Ferretly
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Daniel Bertuccio Daniel Bertuccio
Daniel Bertuccio Australia

Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.

Daniel Bertuccio
Marketing Manager at Eurolinx
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Trent Allan Trent Allan
Trent Allan Australia

We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.

Trent Allan
CTO, Co-founder at Active Place
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Andy Morrey Andy Morrey
Andy Morrey United Kingdom

I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.

Andy Morrey
Managing Director at Magma Trading
Vadim Ivanenko Vadim Ivanenko
Vadim Ivanenko Switzerland

Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.

Vadim Ivanenko
COO at Optherium
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Jason Leffakis Jason Leffakis
Jason Leffakis United States

The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.

Jason Leffakis
Founder, CEO at Function4
View on Clutch
John Boman John Boman
John Boman Sweden

We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.

John Boman
Product Manager at Lexplore
View on Clutch
Tamas Pataky Tamas Pataky
Tamas Pataky Canada

Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.

Tamas Pataky
Head of Product at Stromcore
View on Clutch
Stan Sadokov Stan Sadokov
Stan Sadokov Estonia

Devox is a team of exepctional talent and responsible executives. All of the talent we outstaffed from the company were experts in their fields and delivered quality work. They also take full ownership to what they deliver to you. If you work with Devox you will get actual results and you can rest assured that the result will procude value.

Stan Sadokov
Product Lead at Multilogin
Mark Lamb Mark Lamb
Mark Lamb United Kingdom

The work that the team has done on our project has been nothing short of incredible – it has surpassed all expectations I had and really is something I could only have dreamt of finding. Team is hard working, dedicated, personable and passionate. I have worked with people literally all over the world both in business and as freelancer, and people from Devox Software are 1 in a million.

Mark Lamb
Technical Director at M3 Network Limited
Insights

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FAQ

Also Asked Questions

  • What are MLOps services?

    MLOps services are the engineering and infrastructure that keep machine learning models accurate, auditable, and operable in production. They differ from DevOps by treating data and model behavior as first-class deployable artifacts and from ML consulting by building the running system rather than advising on it. So their main goal is the quality output produced by models.

  • When do we actually need MLOps services?

    The most common use cases include when models are customer-facing, when decisions affect money or safety, when drift will not be caught manually, or when an audit team is asking questions you cannot answer in two minutes. Examples include:

    • Finance & Fintech: real-time fraud detection, credit scoring and risk assessment.
    • Retail & E-commerce: demand forecasting and personalized recommendation engines.
    • Manufacturing & Supply Chain: predictive maintenance, logistics and route optimization.

  • Will you build on our cloud or yours?

    Yours. We are vendor-agnostic and do not resell a platform. Engagements run on your AWS, Azure, GCP, or hybrid environment using your tools and contracts, and you own 100% of the platform code, infrastructure-as-code, runbooks, and model weights as fixed in the contracts.

  • How long does it take to set up MLOps services from zero?

    Roughly from 3 to 5 months for a foundation platform wired to one production model, and 6 to 12 months to migrate a full model portfolio. The assessment phase occurs before you commit to the build budget, so timelines are based on your actual maturity rather than an estimate. If you have a project in mind, let’s connect and discuss.

  • How much MLOps services cost?

    Three-tier MLOps solutions and services providers include:

    1. Assessment from $8,500 fixed-fee (2 weeks)
    2. Foundation Build from $48,000 (8–12 weeks)
    3. Managed MLOps from $14,000 per month

    For your information, the average hourly rate in the U.S. reaches $40–60 for an MLOps engineer, $70–110 for a senior MLOps engineer, and $95–140 per hour for an MLOps architect.

  • How do you detect drift after deployment?

    We run statistical tests on input features and model outputs, plus sliding-window evaluation against a held-out reference set, with alerting integrated into your incident tooling. Methods are selected per data type rather than applied as a single template. Moreover confirmed drift can trigger automated retraining with a regression gate before promotion.

  • What are innovative BPO-MLOps services?

    BPO-MLOps (Business Process Outsourcing paired with Machine Learning Operations) services integrate managed contact center operations with automated AI/ML deployment. They automate routine inquiries, enforce quality monitoring, and predict sales leads, freeing human agents to resolve complex, empathy-driven customer issues.

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