AI & Machine Learning

MLOps Consulting
That Keeps Your ML Models Reliable in Production

Numlytics delivers expert MLOps consulting for enterprises across the US, UK, Australia & UAE - building the ML pipeline automation, model registries, CI/CD workflows, monitoring, and automated retraining infrastructure that keeps machine learning models accurate, reproducible, and maintainable over time. Because a model that worked in deployment six months ago but silently degraded isn't an asset, it's a liability.

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Automated retraining pipelines - models improve without manual intervention
Data drift & model degradation detected before users notice
Azure ML, MLflow, Databricks & GitHub Actions specialists
Up to 50% lower cost vs US/UK AI consulting firms
What We Deliver
1hr
Model drift alerts fired
within 1 hour of detection
100%
Model experiments tracked
and reproducible in MLflow
4wk
Core MLOps infrastructure
live in 4 weeks
50%
Lower cost vs US/UK
AI consulting firms
We build with
Azure ML
MLflow
Databricks
GitHub Actions
Docker / Kubernetes
Apache Airflow
Azure DevOps
Python
What We Build

The Infrastructure That Keeps ML Models Working Over Time

Building a machine learning model is a one-time project. Keeping it accurate, reliable, and trusted in production over months and years is an ongoing engineering challenge. Data distributions shift. Business rules change. Source systems are updated. The world that the model was trained on gradually stops matching the world it's predicting in - and without monitoring, nobody notices until a downstream decision goes wrong.

MLOps (Machine Learning Operations) is the engineering discipline that bridges the gap between model development and production reliability. Reproducible experiment tracking, automated training pipelines, versioned model registries, CI/CD workflows for model deployment, drift detection, and automated retraining, the infrastructure that makes ML systems maintainable, not just deployable.

Our MLOps consulting service implements this infrastructure for your ML estate on Azure ML, Databricks, and MLflow, so your data science team spends time building better models, not manually managing deployments or diagnosing silent accuracy degradation in production.

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The Production ML Problems We Solve
"Our model was accurate at launch now nobody trusts it"
Model accuracy silently degraded over 6 months. No monitoring was in place to detect the drift. Business users lost trust before the data science team even knew there was a problem. The model still runs, but nobody acts on its predictions.
"Retraining our model takes a data scientist 3 days of manual work"
No automated retraining pipeline. Data scientist manually runs preprocessing, retrains, validates, and redeploys every time. Three days of senior engineering time every month, and a model that is always months out of date.
"We can't reproduce last month's model experiment"
No experiment tracking. Hyperparameters changed, training data versions not recorded, model versions not registried. When a newer model underperforms, there's no way to roll back to a known-good checkpoint because nobody knows what that checkpoint was.
"Deploying a new model version is a manual, risky process"
No CI/CD for ML. Every new model version requires a manual deployment process, no automated testing before promotion, no canary or shadow deployment, no one-click rollback. Every deployment is a risk event rather than a routine operation.
What We Deliver

Six MLOps Infrastructure Components for Production ML Reliability

The complete MLOps stack from experiment tracking and model registry through to CI/CD deployment, monitoring, and automated retraining.

Experiment Tracking & Reproducibility

MLflow experiment tracking implementation logging every training run's hyperparameters, metrics, data version, code version, and environment. Every experiment reproducible: given an experiment ID, any result can be recreated exactly. Compare runs, identify the conditions that produced your best model, and roll back confidently.

MLflow tracking server setup
Experiment logging for all training runs
Data versioning & environment capture
Model Registry & Versioning

A centralised model registry Azure ML model registry or MLflow Model Registry - where every trained model is versioned, tagged with its performance metrics, linked to its training run, and given a lifecycle stage (staging → production → archived). One-click promotion and rollback replace manual deployment scripts.

Model registry implementation
Staging / production / archive lifecycle
One-click promotion & rollback
ML Pipeline Automation

Automated, parameterised training pipelines data ingestion, feature engineering, model training, validation, and evaluation orchestrated via Azure ML Pipelines, Databricks Workflows, or Apache Airflow. The same pipeline runs triggered manually, on a schedule, or by data arrival events.

End-to-end training pipeline automation
Parameter configuration & environment management
Schedule & event-triggered execution
CI/CD for ML Deployment

GitHub Actions or Azure DevOps pipelines for model deployment automated evaluation tests before promotion, canary or shadow deployments for safe rollout, integration tests on the serving endpoint, and one-click rollback to the previous production model. Deploying a new model version becomes a safe, automated routine.

GitHub Actions / Azure DevOps ML pipelines
Pre-promotion evaluation gates
Canary deployment & instant rollback
Model Monitoring & Drift Detection

Production model monitoring - data drift detection (input distribution shifts), prediction distribution monitoring, performance degradation alerts where ground truth labels are available, and latency and throughput metrics. Alerts fire within the hour, not after a business user raises a complaint about wrong predictions.

Data drift detection (statistical tests)
Prediction distribution monitoring
Automated Slack / Teams alerts
Automated Retraining Pipeline

Automated retraining triggered by schedule or drift detection ingesting new training data, running the training pipeline, validating the retrained model against the current production model, and promoting automatically if performance thresholds are met. Retraining goes from a 3-day manual task to an overnight automated process.

Drift-triggered or scheduled retraining
Champion/challenger model evaluation
Automated promotion on performance gate
How We Deliver It

From Manual ML Ops to Automated Infrastructure in 4 Phases

Core MLOps infrastructure live in 4 weeks. We start with experiment tracking and registry, the foundations every other component builds on.

ML Estate Audit & Design

Audit your current ML estate- models in production, training processes, deployment methods, monitoring gaps, and reproducibility issues. Design the target MLOps architecture: tooling selection, platform design, pipeline structure, and monitoring strategy. The blueprint for each subsequent phase.

⏱ Week 1
Tracking, Registry & Pipelines

MLflow tracking server, model registry, and automated training pipelines implemented - the foundational layer. Existing training code refactored into parameterised pipelines. All future experiments automatically tracked and registered from this point forward.

⏱ Weeks 2–3
CI/CD & Monitoring

GitHub Actions or Azure DevOps ML deployment pipelines implemented evaluation gates, canary deployment, and rollback capability. Model monitoring infrastructure live — data drift detection, prediction distribution tracking, and alerting active for every production model.

⏱ Weeks 3–5
Retraining Automation & Handover

Automated retraining pipelines configured drift-triggered or scheduled, champion/challenger evaluation, and automated promotion. Full documentation: runbooks, architecture diagrams, alert response guides. Data science team trained on operating and extending the MLOps infrastructure.

⏱ Weeks 5–7
Why Numlytics

Why Choose Numlytics for MLOps Consulting

We've built MLOps infrastructure for data science teams across financial services, SaaS, retail, and manufacturing in the US, UK, and Australia - engineers who understand both ML and production engineering.

ML Engineers, Not Just DevOps Engineers
MLOps requires understanding both ML workflows and production infrastructure. Our MLOps engineers have built and trained models, they understand why experiment tracking matters, what makes a training pipeline brittle, and how to design drift detection that fires on meaningful shifts rather than statistical noise.
MLflow & Azure ML Specialists
Deep expertise in MLflow experiment tracking, model registry, and serving plus Azure ML Pipelines, Databricks Workflows, and GitHub Actions ML deployment. We don't generalise from generic DevOps experience; we've implemented these platforms specifically for ML workflows across multiple engagements.
Foundational Layer First
We build experiment tracking and model registry before CI/CD and monitoring, because without reproducibility and versioning as the foundation, automated deployment and drift detection add complexity without reliability. Every component we add builds on a solid base rather than patching a fragile stack.
Monitoring That Fires When It Matters
Poorly calibrated drift detection creates alert fatigue - statistical tests that fire on minor seasonal variations nobody needs to act on. We tune monitoring thresholds against your historical data to ensure alerts are meaningful and actionable, not noise that trains the team to ignore them.
Runbooks & Operational Handover
Every MLOps engagement includes runbooks what to do when a drift alert fires, how to trigger a manual retraining run, how to roll back a model, how to add a new model to the registry. Infrastructure your team can operate without us, not black-box tooling that requires a consultant to maintain.
Up to 50% Lower Cost
Senior MLOps engineers from India, same platform expertise as US or UK AI consulting firms at up to 50% lower cost. Full timezone overlap, daily standups, and Slack access throughout the engagement.
★★★★★

"We had five machine learning models in production credit risk, churn, propensity, fraud, and demand forecasting. Every one was deployed differently. Some ran as notebooks on a schedule. One was a Python script on a VM that someone had to manually restart when it crashed. None had monitoring. We found out our credit risk model had been degrading for four months when the risk team noticed approval rates dropping without a policy change. Numlytics audited all five models, implemented MLflow experiment tracking and a model registry across all of them, built Azure ML Pipelines for three, added Evidently drift monitoring for all five, and automated the retraining pipeline for our highest-value model. The next time drift hit six months later, we had an alert within 40 minutes, an automatic retraining run overnight, and a retrained model in production before any business user noticed a change in output."

RM
Robert M.
Head of Data Science · Financial Services, United Kingdom

MLOps is the infrastructure layer. These services build the models that run on top of it.

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FAQ

MLOps Consulting FAQs

Common questions before starting an MLOps consulting engagement with Numlytics.

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MLOps (Machine Learning Operations) manages the full lifecycle of machine learning models in production experiment tracking, model versioning, automated training pipelines, CI/CD deployment workflows, model monitoring, drift detection, and automated retraining. It applies DevOps principles to ML to make models reproducible, reliable, and maintainable over time, rather than fragile one-off deployments that degrade silently without anyone noticing.
Model drift is the degradation of a model's accuracy over time as real-world data diverges from training data. Data drift occurs when input feature distributions shift - customer behaviour changes, product mix evolves. Concept drift occurs when the relationship between inputs and target outcome changes. Without monitoring, drift is invisible until a business decision goes wrong. With drift detection, the team is alerted early enough to retrain before accuracy degrades significantly.
A model registry is a centralised system that versions, stores, and manages trained ML models - recording training provenance (data version, hyperparameters, metrics), lifecycle stage (staging, production, archived), and deployment history. Without a registry, rolling back a bad deployment requires manually locating and rerunning training code. With MLflow or Azure ML model registry, rolling back to any previous production model is a one-click operation. See our predictive analytics service for model development.
DevOps manages software code testing, building, and deploying software artefacts. MLOps applies the same principles to ML models, but with additional complexity: models require training data, their quality degrades over time even without code changes (due to drift), and validating them requires domain-specific evaluation metrics rather than just unit tests. MLOps tooling — MLflow, Azure ML Pipelines, Evidently AI - is purpose-built for these ML-specific challenges.
Yes, even a single production model benefits from experiment tracking (reproducibility), a model registry (rollback capability), monitoring (drift detection before accuracy degrades), and an automated retraining pipeline (eliminating manual retraining effort). The ROI is often highest for organisations with just a few high-value models, where a single model degrading silently has significant business impact and the retraining cost in engineering time is a recurring overhead every few months.
Ready to Start?

ML Models That Stay Reliable - Not Just at Launch

Get complete MLOps infrastructure experiment tracking, model registry, CI/CD deployment, drift monitoring, and automated retraining. Core stack live in 4 weeks. Runbooks included. US, UK, Australia & UAE.

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