Data & Analytics
ML Ops & Governance
Models don't fail at the algorithm. They fail at the handover.
12+
capabilities shipped into production systems
We build the layer between a model that works and a model that keeps working: deployment pipelines, feature parity, drift monitoring, retraining gates and rollback. The unglamorous engineering that decides whether the thing you paid to build is still earning its place in eighteen months.
ML Ops & Governance Services
We get models out of notebooks. Then we keep them alive.
From the first honest look at why your models are stuck in staging, to a release process your team runs without us: four services, one senior team from platform assessment to the third year of retraining.
MLOps Assessment & Platform Strategy
We start with why models are not shipping. Handoff gaps, environment drift, no reproducible training, no owner after go-live. You get a maturity read and a platform design scoped to the number of models you actually run, not to a reference architecture built for a thousand.
Output
- Maturity assessment
- Target architecture
- Build vs buy call
Deployment & Serving Pipelines
Reproducible training runs, versioned artefacts, and the path from a registered model to a scoring job that runs on schedule. Batch, real-time or at the edge, with the same feature logic in training and serving, which is where most teams quietly break.
Runs on
- Azure ML
- MLflow
- Kubernetes
- Databricks
- Microsoft Fabric
Monitoring, Drift & Retraining
A model left alone degrades quietly, and nobody notices until a quarter has been planned on bad numbers. We instrument feature drift, prediction distribution, latency and the business KPI underneath, then wire retraining to run on schedule or on trigger, gated on beating the incumbent.
Delivered via
- Drift dashboards
- Automated retraining
- Evaluation gates
Model Governance & Lineage
Every prediction traceable to a model version, a training run and the data that produced it. Approval workflows, model registers, audit trails and the documentation your risk function will ask for the first time a decision is challenged.
Covers
- Model register
- Approval gates
- Lineage
- Audit trails
How we build
Two disciplines. Twelve capabilities production actually needs.
Getting a model out is a delivery problem. Keeping it accurate is an operations problem. Teams that treat the second as an afterthought spend the following year rebuilding what they already shipped, usually while defending a number nobody can trace.
Reproducible training pipelines
Versioned data, parameters and environment, so a run from six months ago produces the same artefact today: the precondition for every audit conversation.
- MLflow
- Azure ML
- DVC
- Docker
Feature stores & parity
One definition used in training and in serving. Where these diverge you get a model that tested well and performs badly, with no obvious reason why.
- Feature Store
- dbt
- Fabric
Batch & real-time serving
Nightly scoring jobs, low-latency endpoints, or both against the same registered model, chosen on how fresh the decision genuinely needs to be.
- Azure ML endpoints
- FastAPI
- Kubernetes
Canary releases & rollback
Shadow traffic, staged rollout and a rollback that is a config change. If reverting requires a redeploy, it will not happen fast enough to matter.
- Canary
- Shadow scoring
- Blue/green
Edge & on-device inference
Quantised, compiled models running at the line or on the device, where latency or connectivity rules out a round trip to the cloud.
- ONNX
- TensorRT
- Azure IoT Edge
Cost & capacity control
GPU scheduling, batching, caching and right-sized instances, with cost per thousand predictions visible per model rather than buried in one cloud bill.
- Autoscaling
- Spot GPU
- Cost tagging
Drift detection
Population stability on every input feature and on the prediction distribution, with thresholds tuned so alerts mean something and get acted on.
- Evidently
- PSI & KS tests
- Azure ML drift
Automated retraining
Runs on schedule or on drift trigger, always gated: a candidate only promotes if it beats the model currently in production on the metric that matters.
- Evaluation gates
- Champion/challenger
- Airflow
Serving observability
Latency, throughput, error rate and cost per prediction traced per model version, instrumented like the production service an endpoint now is.
- OpenTelemetry
- Grafana
- App Insights
Model registry & approvals
A single register of what is live, what is staged and who signed it off, with promotion behind an approval gate rather than a merge.
- MLflow Registry
- Azure ML
- Entra ID
Lineage & auditability
Every prediction traceable to a model version, a training run and the source rows behind it. The question always arrives eventually.
- Purview
- Data lineage
- Audit trails
Responsible AI in production
Fairness metrics tracked live rather than signed off once at launch, because a model can pass a bias review in March and fail it by September.
- Fairlearn
- SHAP
- Model cards
Most engagements start on the Monitor & Govern track. Teams rarely call us because deployment is hard; they call us because something has been quietly wrong for a while and nobody can prove when it started.
How we engage
Find the stage that matches where you are
Four ways to work with us, from an honest read on why models are stuck in staging to a platform your own team runs without us in the room.
Stage: Assessment
What you’re asking: “Why are our models stuck in staging?”
Our service: MLOps Assessment & Platform Strategy
Timeline: 2–4 weeks
Stage: Foundation
What you’re asking: “Get one model deployed properly, end to end.”
Our service: Deployment & Serving Pipelines
Timeline: 8–12 weeks
Stage: Scale
What you’re asking: “Make the next ten models cheap to ship and safe to run.”
Our service: Platform Engineering & Governance
Timeline: 12–20 weeks
Stage: Operate
What you’re asking: “Run it for us, and tell us when something moves.”
Our service: Managed ML Operations
Timeline: Continuous
Where it applies
Industries we run machine learning operations for
The operational discipline stays the same everywhere. What changes is how fast the data moves underneath the model, and who asks to see the audit trail.
- 01
Healthcare & Life Sciences
Validated pipelines with full lineage and human approval on promotion, because a model touching a patient record has to be explainable months after the fact.
Explore the industry - 02
Financial Services & Insurance
Model risk management, challenger models and audit trails built to satisfy a regulator rather than to satisfy the team that built them.
Explore the industry - 03
Logistics & Transportation
High-frequency retraining where conditions shift weekly, and serving that holds latency when the network is the least reliable part of the stack.
Explore the industry - 04
Manufacturing & Supply Chain
Edge deployment on the line, model versioning per site, and rollback that does not require someone to physically attend a plant.
Explore the industry - 05
Real Estate & Data Centers
Telemetry-driven models across a distributed estate, with capacity and power predictions monitored per site rather than in aggregate.
Explore the industry - 06
Retail & CPG
Seasonal retraining cycles, thousands of SKU-level models, and the cost engineering that makes running them at that scale affordable.
Explore the industry
Platform focus
Native to the Azure estate your models already run in.
We build on Azure Machine Learning and Microsoft Fabric so pipelines inherit the identity, networking and governance your platform team already approved. No parallel stack to secure, no second set of credentials to rotate, and no argument with your security function three weeks before go-live.
- Azure Machine Learning
- Microsoft Fabric
- MLflow
- Azure DevOps
- Kubernetes
- Microsoft Purview
Governed by default
Entra identity, managed endpoints and Purview lineage carried through from source rather than bolted on afterwards.
One pipeline definition
The same code path from dev to production, so environment drift stops being the reason a release fails.
Beyond Azure
The same discipline extends to AWS SageMaker, GCP Vertex, Databricks and on-premise GPU estates.
Why Praval
Why teams bring us in
We have seen how it breaks
Training-serving skew, silent drift, a model nobody owns after the consultant left. The failure modes repeat, and we design against the ones we have already met.
Right-sized, not reference architecture
If you run four models, you do not need the platform a company running four hundred needs. We build for the scale you are at plus one step, not for a diagram.
We hand it back
The goal is your team running this without us. Runbooks, training and a deliberate exit are part of the engagement, not an upsell at the end of it.
Senior team, start to finish
The people who assess the platform are the people who build and operate it. No handoff to a junior bench once the statement of work is signed.
Start with what’s stuck
Tell us which model has been sitting in staging for three months. We’ll tell you honestly what it would take to ship it.
No slide deck of maturity models; a working point of view on your platform, from a senior team.
Related
Related work
3 of 3 shown
AI & Machine Learning
Predictive, classification and computer-vision models built on your own data, each with a measured baseline and an honest read on where it breaks.
ServiceGenerative & Agentic AI
Generative and agentic AI systems: reasoning, tool-use and multi-agent orchestration wired into the platforms you already run, each with a baseline, guardrails and an evaluation harness.
PlatformMicrosoft & Azure
Azure consulting, migration, managed services and governance, plus the Microsoft data and low-code estate: Fabric, Power BI and Power Platform.
Questions
Common questions
- How do we know our models have drifted?
- Usually you don't, and that is the problem. Without monitoring, drift shows up as a business number quietly getting worse: a forecast that needs more manual override, an alert queue nobody trusts. We instrument the input distribution and the prediction distribution so the degradation is visible before it reaches the P&L.
- Do we need a full MLOps platform for four models?
- Almost certainly not. At that scale a scheduled pipeline, a model registry and a monitoring dashboard cover most of the value. Platform investment pays off when the cost of shipping the next model is what is slowing you down. We will tell you which side of that line you are on.
- What is training-serving skew and why does it matter?
- It is when a feature is calculated one way in training and slightly differently at inference: a rounding rule, a time window, a null handled elsewhere. The model tests well and underperforms in production with no obvious cause. It is the single most common defect we find, and a shared feature definition is the fix.
- Can you work with models you didn't build?
- That is most of our work. We take on models built in-house or by another partner, get them reproducible first, then productionise them. If the model itself does not hold up under backtesting, we will say so before wrapping a pipeline around it.
- Who owns the platform when you leave?
- Your team, unless you have asked us to operate it. We build on your cloud tenancy with your identity provider, document runbooks as we go, and run handover sessions before the engagement closes. If you would rather we ran it, managed operations is a service, not the default outcome of us building it.
Why Praval
How we work with you.
Industry expertise
Seasoned professionals with deep industry knowledge and hands-on experience driving digital acceleration across sectors.
Client-centric approach
We prioritise understanding your challenges, goals and culture, and deliver solutions tailored to them rather than to a template.
Proven methodologies
Industry-leading frameworks and best practice, giving a structured and repeatable route to the outcome you asked for.
Collaborative partnership
We work as an extension of your organisation: transparency and agility during the engagement, and a handoff that holds after it.
- 01
Initial consultation
We evaluate your current systems and identify where the value is.
- 02
Customized plan
We design a solution scoped to your business, not to a template.
- 03
Design & development
We build and transition with minimal disruption to live operations.
- 04
Monitoring & support
Continuous oversight and support keep the estate healthy afterwards.