Azure Machine Learning · Certified Partner India

Train & Deploy AI Models
at Scale with Azure ML

SchwettmannTech designs and operationalises enterprise machine learning workflows on Azure Machine Learning — from data preparation and model training to MLOps pipelines, responsible AI governance, and production deployment integrated with Dynamics 365 and Power BI for business-ready AI in India.

See Our Work

Certified Azure ML Partner

50+ ML Models in Production in India

Responsible AI & MLOps Governance

50+

ML Models in Production

40%

Avg Prediction Accuracy Uplift

8 wks

Data to Production Model

95%

Model Monitoring Uptime SLA

Azure ML Studio · Experiment Dashboard

Running

Live Models

↑ 3 new

94.2%

Avg Accuracy

↑ +2.1%

8 wks

Avg to Prod

Stable

Drift Alerts

✓ Clean

Demand Forecasting — Retail Client

XGBoost · 92% accuracy · D365 F&O integrated · weekly retraining

Live

Fraud Detection Model — NBFC Client

🔒 Anomaly detection · 99.7% precision · real-time scoring endpoint

Active

Churn Prediction — Telecom

Random Forest · retrained weekly · Power BI dashboard surfaced

Running

MLOps Pipeline — Manufacturing QC

Azure DevOps · CI/CD model deploy · 24×7 drift monitoring

Deployed

Azure ML Copilot: "Generate a training pipeline for tabular classification with automated feature engineering" — pipeline YAML created in 30 seconds.

50+

Azure ML Projects Delivered in India

40%

Average Model Accuracy Improvement

8 wks

Data to Production-Grade Model

3×

Faster MLOps vs Manual Deployment

Azure AutoMLMLOps PipelinesResponsible AIModel RegistryFeature StoreBatch InferenceReal-Time EndpointsAzure ML ComputePrompt FlowONNX Export Azure AutoMLMLOps PipelinesResponsible AIModel RegistryFeature StoreBatch InferenceReal-Time EndpointsAzure ML ComputePrompt FlowONNX Export

Azure ML Services

End-to-End Azure Machine Learning for Indian Enterprises

Six ML capability areas — from exploratory data science and AutoML to full MLOps pipelines, responsible AI governance, and D365 integration at enterprise scale.

Model Development & Training

Build supervised, unsupervised, and deep learning models using Azure ML Studio, Jupyter notebooks, and VS Code integration. We work with Python (scikit-learn, PyTorch, TensorFlow, XGBoost) leveraging Azure ML Compute clusters for GPU-accelerated training on large datasets — from demand forecasting and fraud detection to NLP and computer vision.

PyTorch · TensorFlow · GPU Compute

AutoML & Automated Feature Engineering

Accelerate model development with Azure AutoML — automatically trains and tunes hundreds of model and hyperparameter combinations to find the best-performing algorithm for your dataset. AutoML handles feature engineering, missing value imputation, and model selection, cutting data science time by 60–80% for standard tabular classification, regression, and time-series forecasting problems.

AutoML · Hyperparameter Tuning · Feature Store

MLOps — CI/CD for Machine Learning

Implement MLOps discipline — version-controlled models in Azure ML Model Registry, automated retraining pipelines triggered by data drift, GitHub Actions / Azure DevOps CI/CD for model deployment, A/B testing between model versions, and automated rollback on performance degradation. We bring software engineering best practices to your ML lifecycle.

Model Registry · Azure DevOps · Drift Detection

Real-Time & Batch Inference

Deploy models as real-time scoring endpoints (Azure Kubernetes Service for high-throughput) or batch inference pipelines (Azure ML Batch Endpoints for overnight scoring runs). We optimise models for inference latency, manage endpoint scaling, and integrate scoring APIs directly into Dynamics 365, Power Apps, and custom .NET/Node.js applications.

AKS Endpoints · Batch Scoring · API Integration

Responsible AI & Model Governance

Implement Microsoft's Responsible AI principles — model explainability with InterpretML/SHAP, fairness assessment across demographic groups, data lineage tracking, and compliance documentation. We configure Azure ML's Responsible AI dashboard for every production model, providing audit trails for RBI, SEBI, and IRDAI-regulated AI use cases in India.

InterpretML · Fairness · Explainability · Audit

ML-Powered Dynamics 365 Integration

Embed ML predictions directly into Dynamics 365 workflows — lead scoring in D365 Sales, predictive maintenance alerts in D365 Field Service, demand forecasts in D365 Finance, and churn risk scores in D365 Customer Insights. We build the integration layer between Azure ML endpoints and D365 using Power Automate, Azure Functions, and Dataverse plugins.

D365 Sales · Field Service · Customer Insights

Platform Capabilities

Everything We Build on Azure Machine Learning

The complete Azure ML platform — designer, AutoML, MLOps, responsible AI, and Dynamics 365 integration for India's enterprise AI teams.

🧠ML Platform

Azure ML Studio — End-to-End

The complete ML development environment — dataset management, experiment tracking, hyperparameter tuning, model registry, and deployment pipelines — all in one governed workspace with Azure AD RBAC, cost controls, and audit logs across dev, test, and production environments.

Azure ML Compute Clusters & Instances
Automated Hyperparameter Sweeps
Experiment Tracking & Comparison
Git-Integrated Notebooks & VS Code

Data Platform

Feature Store & Data Assets

Centralised feature store for reusable ML features — connect to Azure Data Lake, Azure Synapse, Azure SQL, and Dataverse. Versioned datasets with data lineage. Automated data profiling and drift detection to catch upstream issues before they degrade model performance.

Reusable Feature Definitions
Data Lineage & Versioning
Drift Detection Alerts

MLOps

CI/CD Model Pipelines

Fully automated MLOps: code commit triggers retraining pipeline, model evaluation vs baseline, champion/challenger comparison, approval gate, and deployment to AKS endpoint — all automated via Azure DevOps or GitHub Actions with Teams notifications at each stage.

Responsible AI

Explainability & Fairness

Azure Responsible AI dashboard for every production model — feature importance, counterfactual analysis, fairness metrics across protected groups, and error analysis. Mandatory for RBI, IRDAI, and SEBI AI deployments in India where audit trails are required.

Retraining

Automated Model Retraining

Schedule or trigger model retraining based on data drift thresholds — new training data automatically incorporated, model performance validated against holdout set, and promotion to production only when accuracy exceeds threshold. Zero manual retraining intervention.

🔌Integrations

Azure ML + Power Platform & D365

REST scoring API integration with Power Apps, Power Automate, Dynamics 365, and custom applications — real-time predictions surfaced in D365 dashboards, Power BI, and Teams alerts. Azure Functions as lightweight middleware for complex integration patterns.

Real-Time API Endpoints
Power Automate ML Connector
D365 Plugin Integration

☁️Infrastructure

Compute & Cost Optimisation

Right-sized ML compute — CPU/GPU clusters for training, low-latency AKS for inference, and serverless batch endpoints for overnight scoring. Azure Spot Instances for training cost reduction (up to 90% savings). Automated compute shutdown to eliminate idle costs across all environments.

GPU Clusters for Deep Learning
Azure Spot Instance Training
Serverless Batch Inference

Delivery Methodology

Our 4-Phase Azure ML Engagement Framework

A structured approach used across 50+ Azure ML projects in India — from business problem definition to live ML model in production.

Weeks 1–2

Business Problem & Data Assessment

Define ML objective & validate data

Define the ML problem statement (classification, regression, forecasting, NLP, CV), success metrics, and data requirements. Audit available datasets — volume, quality, feature completeness, label availability. DPDP Act privacy assessment. Go/no-go recommendation before model development.

Problem DefinitionData AuditPrivacy Assessment

Weeks 2–6

Feature Engineering & Model Training

Data pipeline + experiment runs

Build Azure ML data pipelines from source systems. Feature engineering, encoding, normalisation. Run AutoML + custom model experiments. Hyperparameter tuning. Experiment tracking and model comparison. Responsible AI dashboard generated for top candidate models.

AutoMLFeature StoreExperiment Tracking

Weeks 6–8

Model Validation & MLOps Pipeline

Validate accuracy & build CI/CD

Holdout test set evaluation, business stakeholder review of model explanations and fairness metrics. MLOps pipeline built — CI/CD for automated retraining, model registry, drift monitoring, and A/B deployment framework. Load testing of inference endpoint.

MLOps CI/CDChampion/ChallengerLoad Testing

Week 8+

Production Deployment & Monitoring

Live model with 24×7 monitoring

Model deployed to production AKS endpoint or batch pipeline. Integration with D365/Power Apps/Power BI completed. 24×7 monitoring for prediction drift, data quality, and endpoint health. Monthly model performance reviews and quarterly retraining cycles.

AKS DeploymentDrift MonitoringD365 Integration

Industries

Azure ML for Every Industry in India

Industry-specific ML use cases deployed and in production across India's key sectors.

BFSI & Credit

Manufacturing QC

Retail Forecasting

Healthcare AI

Telecom Churn

Construction Risk

Pharma R&D

Energy Demand

Responsible AI for India's Regulated Sectors

Azure ML's Responsible AI dashboard provides model explainability, fairness metrics, and audit trails required for AI deployment in RBI-regulated BFSI, IRDAI-regulated insurance, and SEBI-regulated capital markets — configured for every production deployment.

Business Impact

Proven Results from Azure ML Deployments

Outcomes from SchwettmannTech's Azure ML engagements across Indian enterprises — measured at 90 days post model go-live.

40%

Average improvement in prediction accuracy over rule-based baseline systems

8 wks

From data availability to production-grade ML model deployment

3×

Faster MLOps retraining cycles vs manual model update processes

60%

Reduction in data science time through AutoML and feature automation

Customer Stories

What Our Azure ML Clients Say

"SchwettmannTech built our credit scoring model on Azure ML in 8 weeks. Loan default prediction accuracy improved 42% over our previous rule-based system. The model is now integrated directly into our D365 Finance loan origination workflow — underwriters see the ML risk score alongside the application. RBI audit passed with full model explainability documentation provided."

Rajesh Sharma

Head of Analytics · Leading NBFC, Mumbai

"Our demand forecasting model on Azure ML reduced excess inventory by 28% and stockouts by 35%. SchwettmannTech built the AutoML pipeline, integrated it with our D365 Finance demand planning module, and set up automated weekly retraining. The MLOps pipeline means the model updates itself — our team didn't need to hire a single data scientist to maintain it."

Priya Kumar

VP Operations · Auto Parts Manufacturer, Pune

"We needed a patient readmission risk model that could explain its predictions to doctors — not a black box. SchwettmannTech delivered an interpretable Random Forest model with Azure ML's Responsible AI dashboard showing feature importance per patient. Our clinical team trusts it because they can see exactly why the model flags a patient as high-risk."

Arun Mehta

CTO · Healthtech Platform, Bangalore

FAQs

Common Azure ML Questions

Have a specific ML use case? Our Azure ML architects will assess feasibility and data requirements in a free scoping call.

Talk to an ML Architect

We work across the full spectrum: classification (fraud detection, churn prediction, credit scoring), regression (demand forecasting, price optimisation, predictive maintenance), time-series forecasting (inventory, energy demand, financial), NLP (document classification, sentiment analysis, entity extraction), and computer vision (quality inspection, OCR, defect detection). We start every engagement with a feasibility assessment to validate that available data is sufficient for the ML objective before committing to a build.

No — Azure ML's AutoML dramatically reduces the need for expert data scientists for standard ML problems. AutoML selects the best algorithm and hyperparameters automatically, reducing data science effort by 60–80% for standard tabular use cases. You still need data engineering to build quality training datasets, and our team handles the MLOps infrastructure. We've delivered production ML models for organisations with zero in-house data scientists, transferring skills to your team as part of the engagement.

We implement data privacy from the start: training data stored in Azure India regions, personal data identified and minimised through DPDP Act assessment, differential privacy techniques applied where needed for sensitive datasets, and model training never stores raw PII in model artefacts. For BFSI clients, we provide full model documentation — training data description, feature definitions, validation methodology, fairness assessment — for RBI and IRDAI regulatory submissions.

Azure ML models are deployed as REST endpoints that any application can call. For D365 integration, we use Power Automate's Azure ML connector, Dataverse plugins for server-side scoring within D365 workflows, or Azure Functions as lightweight middleware. Power BI has a native Azure ML integration — analysts can call ML models directly from Power Query M to enrich datasets with predictions. We configure all integration patterns as part of the production deployment phase.

We offer three managed service tiers: (1) Monitor — automated alerts when accuracy drops below threshold, data drift reports, and monthly performance reviews; (2) Manage — proactive retraining when drift is detected, endpoint scaling management, and quarterly model refresh; (3) Partner — dedicated ML engineer embedded in your team with continuous feature engineering and new model development on retainer. All tiers include incident response SLA and Responsible AI dashboard updates.

Deploy Your First Azure ML Model in 8 Weeks

Book a free 2-hour Azure ML Feasibility Assessment. We'll evaluate your data, identify your best ML use cases, and deliver a roadmap with expected accuracy benchmarks — no commitment required.

Azure OpenAI Service Azure AI Studio

Train & Deploy AI Modelsat Scale with Azure ML

End-to-End Azure Machine Learning for Indian Enterprises

Everything We Build on Azure Machine Learning

Azure ML Studio — End-to-End

Feature Store & Data Assets

CI/CD Model Pipelines

Explainability & Fairness

Automated Model Retraining

Azure ML + Power Platform & D365

Compute & Cost Optimisation

Our 4-Phase Azure ML Engagement Framework

Azure ML for Every Industry in India

Proven Results from Azure ML Deployments

What Our Azure ML Clients Say

Common Azure ML Questions

Deploy Your First Azure ML Model in 8 Weeks

Train & Deploy AI Models
at Scale with Azure ML