Predictive Analytics & ML Models
Production-Grade Machine Learning
End-to-end MLOps pipelines, feature stores, model monitoring, and time series forecasting - engineered for reliability at scale.
Why This Matters
ML in Production Is an Engineering Problem.
Building a machine learning model in a Jupyter notebook is easy. Deploying it to production, monitoring its performance, detecting data drift, and retraining it automatically - that's the hard part. 87% of ML models never make it to production (Gartner). The gap isn't AI expertise - it's MLOps engineering.
Production ML requires a full stack: feature stores for consistent data transformation, experiment tracking for reproducibility, CI/CD pipelines for model deployment, serving infrastructure for low-latency inference, and monitoring systems for drift detection. Each component must be reliable, scalable, and auditable.
We build end-to-end MLOps platforms using battle-tested tools - MLflow for experiment tracking, Kubeflow for pipeline orchestration, Feast for feature stores, and Evidently AI for monitoring. Our models run in production for years, not days.
Our Tech Stack
Production-Grade Tools We Deploy
ML Frameworks
AutoML
Feature Engineering
Experiment Tracking
Pipeline Orchestration
Model Serving
Monitoring & Drift Detection
Data Versioning & Platforms
Architecture Deep-Dive
How We Build It
End-to-End MLOps Pipelines
CI/CD for ML with automated training, validation, and deployment. Kubeflow + MLflow integration. Model registry with staging/production promotion gates.
- Kubeflow Pipelines for Kubernetes-native ML workflow orchestration
- MLflow for experiment tracking, model registry, and deployment
- Automated training pipelines triggered by data changes or schedules
- Model validation gates: accuracy thresholds, bias checks, performance benchmarks
- Blue-green and canary deployments for safe model rollouts
- Git-based versioning of training code, data, and model artifacts (DVC)
Feature Store Architecture
Centralized feature engineering with Feast/Tecton. Online and offline feature serving. Feature versioning, lineage, and reuse across teams.
- Feast for centralized feature definitions shared across teams
- Online serving: sub-10ms feature retrieval for real-time inference
- Offline serving: point-in-time correct features for training
- Feature lineage and provenance tracking for audit compliance
- Feature versioning: roll back to any historical feature definition
- Feature reuse: compute once, use across 10+ models
Model Monitoring & Drift Detection
Real-time performance monitoring with Evidently AI. Data drift, concept drift, and prediction drift alerting. Automated retraining triggers.
- Evidently AI dashboards for data quality and drift monitoring
- Statistical drift detection: PSI, KL divergence, Wasserstein distance
- NannyML for performance estimation without ground truth labels
- Automated retraining triggers when drift exceeds thresholds
- Model performance SLAs with alerting to PagerDuty/Slack
- A/B testing framework for comparing model versions in production
Time Series & Forecasting
Prophet, NeuralProphet, Temporal Fusion Transformers, and N-BEATS for multi-horizon forecasting with uncertainty quantification. Hierarchical forecasting for supply chain.
- Prophet and NeuralProphet for interpretable business forecasting
- Temporal Fusion Transformers for multi-horizon probabilistic forecasting
- N-BEATS for univariate time series with state-of-the-art accuracy
- Hierarchical forecasting: reconciled forecasts across product/region levels
- Uncertainty quantification: prediction intervals, not just point forecasts
- Anomaly detection on time series with seasonal decomposition
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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