Azure Machine Learning & MLOps
Azure Machine Learning and MLOps services
Konsolute builds the platform, pipelines and controls that take machine learning from a data scientist's notebook to a reliable production service. We design Azure Machine Learning environments, automate training and deployment, and monitor models so they stay accurate, secure and auditable.
BUSINESS OUTCOMES
Get more models into production, and keep them working
Most machine learning projects stall between proof of concept and production. Manual handovers, untracked experiments and no monitoring mean models are slow to release and quietly lose accuracy. MLOps fixes that with engineering discipline.
Faster time to production
Automated pipelines take a model from training to deployment in days rather than months.
Reproducible results
Every model is versioned with the data, code and settings used to train it, so results can be repeated and explained.
Lower compute costs
Right-sized compute clusters that scale to zero when idle, with cost tracked per project and model.
Models you can trust
Monitoring for drift, accuracy and fairness, with clear owners and approval gates before any release.
HOW TO WORK WITH US
Three ways to start with MLOps
Start with a fixed-price review of how you build and release models today, set up a production-ready Azure Machine Learning platform, then let us run and improve it with you.
Start here
MLOps readiness review
For: Teams with models in notebooks, manual deployments or no production monitoring
What you get
Then
Azure ML platform build
For: Organisations ready to put models into production on Azure
What you get
Extend
Managed MLOps
For: Teams that want models monitored and maintained for them
What you get
OUR MACHINE LEARNING SERVICES
From experiment to production machine learning
From the first use case to models running reliably in production, we cover the full machine learning lifecycle on Azure.
Use-case and model design
Frame the business problem, assess the data and agree how model success will be measured before any build starts.
Azure ML platform engineering
Design secure Azure Machine Learning workspaces, compute, networking and access, deployed as infrastructure as code.
Feature and data pipelines
Build repeatable pipelines that prepare training data from Fabric, Azure data stores and business systems, with quality checks built in.
Model development
Develop, train and evaluate models for forecasting, classification, anomaly detection and optimisation using Azure Machine Learning.
CI/CD and deployment
Automate testing, approval and release of models to managed endpoints or batch jobs using Azure DevOps or GitHub Actions.
Monitoring and responsible AI
Track drift, accuracy and fairness in production, with explainability and audit trails for every model decision.
POWERED BY MICROSOFT
Built on Azure Machine Learning and the wider Microsoft stack
We use Azure Machine Learning as the core of your MLOps platform, connected to your data in Fabric or Azure and released through the DevOps tools your teams already use.
Workspaces and compute
A secure, well-organised Azure Machine Learning environment for your data scientists and engineers.
Experiments and model registry
Track every experiment and promote approved models through a single, shared model registry.
ML pipelines
Repeatable pipelines for data preparation, training, evaluation and scoring, run on a schedule or on demand.
CI/CD for machine learning
Release models with the same rigour, testing and approvals as your application code, across environments.
Model deployment
Serve models in real time or in batch, integrated with the applications and processes that use them.
Production monitoring
Know when a model is drifting or losing accuracy before it starts to affect business decisions.
Secure ML platform
Keep training data, models and endpoints inside your network and security boundary at every stage.
Responsible AI and audit
Evidence that models are fair, explainable and formally approved before anyone relies on them.
GOVERNANCE AND SECURITY
Machine learning you can explain and audit
When a model influences real decisions, you need to show how it was built, who approved it and how it is performing. Our security-cleared consultants design these controls in from the start.
Named model owners
Every model has an accountable owner, a documented purpose and agreed performance thresholds.
Controlled access
Role-based access to data, experiments, models and endpoints, separated by environment.
Lineage and reproducibility
Trace any prediction back to the model version, training data and code that produced it.
Fairness and explainability
Check models for bias, explain their outputs and define where human review is required.
OUR APPROACH
From notebook to production in four stages
We build your MLOps capability in stages, proving it on a real model first so your team sees value early.
1. Review and prioritise
Assess your current models, tools, data, release process and team skills.
You receive: an MLOps maturity report and costed roadmap.
2. Design the ML platform
Define the Azure Machine Learning architecture, environments, pipelines and approval gates.
You receive: an agreed platform design and delivery plan.
3. Build and prove
Build the platform and take a priority model through the full pipeline into production.
You receive: a production model with automated training and deployment.
4. Monitor and scale
Switch on monitoring, hand over operations and onboard further models.
You receive: runbooks, dashboards and a model backlog.
CASE STUDY
The governed data foundation machine learning depends on
Reliable models need reliable data. For Nuclear Waste Services, Konsolute built a governed Azure data platform on a medallion architecture, giving the organisation trusted, well-understood data ready for analytics and future machine learning.
WHAT YOUR TEAM RECEIVES
A production-ready MLOps platform your team can run
Deliverables reflect the agreed scope. Your team receives the design, a working platform, a model in production and the procedures needed to run and extend it.
Platform design
Azure Machine Learning architecture, environments, security model and documented design decisions.
Automated pipelines
Training, evaluation and deployment pipelines in source control, deployed as infrastructure as code.
Model in production
A priority model deployed with monitoring, alerts and evaluation results against agreed measures.
Operations and model backlog
Runbooks, model cards, ownership records and a prioritised backlog of further models to onboard.
FAQ
Azure Machine Learning and MLOps FAQs
Azure Machine Learning, MLOps, costs and governance, explained.
MLOps (machine learning operations) applies DevOps practices to machine learning. It automates how models are trained, tested, deployed and monitored, so they reach production faster and stay accurate once they are there.
Azure Machine Learning is Microsoft’s cloud service for building, training, deploying and managing machine learning models. It provides workspaces, compute, pipelines, a model registry, managed endpoints and monitoring in your own Azure subscription.
Azure AI services provide ready-made and generative AI capabilities such as Azure OpenAI and Document Intelligence. Azure Machine Learning is for building and operating your own models trained on your data. See our Azure AI consultancy for generative AI solutions.
Yes. This is the most common starting point. We refactor notebook code into tested, repeatable pipelines, register the models and automate their deployment and monitoring.
Yes. Azure Machine Learning connects to Microsoft Fabric, Azure Data Lake, Azure SQL and other sources. If your data needs work first, see our AI data readiness assessment.
Yes. Azure Machine Learning supports Python, scikit-learn, PyTorch, TensorFlow, XGBoost and MLflow, so your data scientists can keep using the tools they know.
We monitor data drift, prediction drift and accuracy against real outcomes. When agreed thresholds are breached, alerts are raised and retraining can be triggered automatically or with approval.
Yes. Our security-cleared consultants design Azure Machine Learning with private networking, managed identities, role-based access and full audit trails, and have delivered governed data platforms in the nuclear sector.
The MLOps readiness review is fixed price. Platform build and managed MLOps are then scoped from the review findings, with Azure compute and storage costs shown separately.
Yes. Our managed MLOps service monitors your models and platform, handles retraining and updates, and reports on model health each month.
TAKE THE NEXT STEP
Start with an MLOps readiness review
In two to three weeks you will know how mature your machine learning practice is, what a production-ready Azure platform looks like for you and what it will cost. Fixed price, no obligation to continue.