From Insight to Predictive Impact
Applied Machine Learning
We build and deploy ML models that solve real operational and strategic challenges, helping organisations move from reactive reporting to proactive, predictive decision-making.
MLOps on Azure ML, Databricks and Microsoft Fabric
Data & AI on Azure 
Microsoft Fabric Featured Partner 
Infrastructure (Azure) 
Digital App Innovation (Azure) Key Capabilities
Predictive Analytics, Forecasting, and Anomaly Detection
Build ML models that forecast demand, detect anomalies, and surface patterns in your data, enabling proactive decision-making and early intervention across your operations.
Classification and Recommendation Models
Design and deploy classification and recommendation systems that automate categorisation, personalise experiences, and drive intelligent decision support across your business.
Model Monitoring, Performance Optimisation, and ML Lifecycle Management
Establish comprehensive monitoring and optimisation processes that ensure your ML models maintain accuracy, performance, and relevance throughout their production lifecycle.
Continuous Model Integration & Enterprise MLOps Solutions
Implement enterprise-grade MLOps pipelines that automate model training, validation, and deployment with continuous integration practices for reliable, repeatable ML delivery.
Machine Learning Deployment and ML Pipeline Automation
Design and implement automated ML pipelines that handle data ingestion, feature engineering, model training, and production deployment, reducing manual effort and accelerating time-to-value.
Less manual effort in model training and deployment
To a first model running in MLOps production
Models tracked with lineage, metrics and approvals
Faster retraining cycles vs. manual processes
Where Applied Machine Learning drives value
Production ML on Azure ML or Databricks
Operationalise models built by your data scientists with MLflow tracking, registries, managed endpoints and drift monitoring.
Demand and revenue forecasting
Automate the full forecast lifecycle — feature engineering, training, backtesting, deployment, refresh — so planning teams always see a current view.
Classification and recommendation
Productionise models that score leads, triage cases, categorise documents or recommend next-best-action inside business workflows.
Predictive maintenance and anomaly detection
Stand up streaming inference over IoT or transaction feeds so issues are surfaced in minutes, not after the fact.
GenAI and RAG operationalisation
Wrap GenAI and RAG solutions in the same MLOps rigour as traditional ML: evaluation datasets, regression tests, canary deploys and monitoring.
Regulated model risk
Deliver model validation, challenger frameworks and evidence packs for financial services and healthcare model risk teams.
A proven delivery approach
- 01 Step
Assess
Review existing models, tooling, data pipelines and release practices to identify the highest-value MLOps improvements.
- 02 Step
Design
Agree target architecture across Azure ML, Databricks, MLflow, Fabric and DevOps with clear environment and promotion strategy.
- 03 Step
Build
Implement reusable pipelines for training, evaluation, deployment and monitoring, wired into CI/CD and your data platform.
- 04 Step
Run
Operate models with drift, performance and fairness monitoring, automatic retraining and alerting — optionally as a managed service.
Frequently asked questions
What platforms do you use for MLOps?
Primarily Azure Machine Learning, Databricks (MLflow, Model Serving, Feature Store) and Microsoft Fabric Data Science, integrated with Azure DevOps or GitHub Actions. The choice depends on where your data lives and the rest of your Microsoft estate.
How long does MLOps take to implement?
A minimum viable MLOps platform — pipelines for training, deployment, monitoring and CI/CD — typically takes 8–12 weeks. Onboarding subsequent models is much faster, often a couple of weeks once the pattern is in place.
We already have models in production — can you uplift them?
Yes. We often inherit notebook-based or hand-deployed models and migrate them into a governed MLOps pipeline without a full rewrite, delivering most of the value of a greenfield build at a fraction of the cost.
How do you monitor models in production?
We monitor data drift, concept drift, performance against ground truth, infrastructure health and (for GenAI) prompt / response evaluation. Alerts feed into the same observability stack your platform team already uses.
Does MLOps apply to GenAI?
Yes — we often call it LLMOps. The principles are the same: versioned prompts, evaluation datasets, regression tests, safe rollout patterns and production monitoring for quality, cost and safety.
Can Synapx run the MLOps platform for us?
Yes. Synapx-as-a-Service provides ongoing model operations, retraining, monitoring and enhancement by the same UK-based engineers who built your platform.
Ready to Get Started?
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