Platform

ML Pipeline

From notebook to production in under two weeks.

Mwzn ML Pipeline is a fully managed end-to-end machine learning platform — covering feature engineering, experiment tracking, model registry, one-click deployment, and drift monitoring — so your data science teams spend time on models, not infrastructure.

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Managed feature store (offline + online)
Distributed training (CPU & GPU)
Hyperparameter optimization
Automated ML (AutoML)
Model explainability (SHAP, LIME)
Real-time and batch inference

2 wks

Avg. time to production

80%

Reduction in deployment time

99.9%

Model availability SLA

500+

Model versions managed

[ Capabilities ]

Everything you need, nothing you don't

Built for production from day one — no cobbled-together open-source toolchain required.

Managed Feature Store

Centralize feature computation with a unified offline/online serving API. Version every feature transformation, catalog features for team reuse, and eliminate training-serving skew permanently.

Experiment Tracking

Log every training run with full reproducibility — dataset version, hyperparameters, code commit, and all metrics. Compare runs visually and roll back to any prior experiment instantly.

Model Registry & Lifecycle

Promote models through Staging → Production → Archived stages with enforced evaluation gates. One-click A/B deployment, canary rollouts, and instant rollback from the registry UI.

One-Click Deployment

Deploy any registered model to a low-latency REST endpoint with auto-scaling, load balancing, and request batching — without writing any deployment code or managing Kubernetes directly.

Drift Detection & Monitoring

Continuous statistical monitoring of input feature distributions and prediction distributions. Auto-triggers retraining pipelines when drift exceeds configurable thresholds.

Automated Retraining Pipelines

Schedule or trigger automated retraining on new data, data drift events, or calendar schedules. Retraining pipelines run the full training → evaluation → promotion lifecycle without human intervention.

[ Process ]

How it works

A proven delivery process refined across 150+ enterprise AI deployments.

Connect your data sources and define feature transformations in the Feature Store. The platform handles offline batch computation, online serving, and versioning automatically.

ML Pipeline

From notebook to production in under two weeks.

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[ Use Cases ]

Built for your industry

Financial Services

Credit Scoring & Risk Models

A regional bank migrated 12 legacy credit risk models to Mwzn ML Pipeline — reducing model deployment time from 6 weeks to 3 days and enabling monthly model refresh cycles instead of annual.

95%

Reduction in deployment time

Retail & FMCG

Demand Forecasting at Scale

A consumer goods company deployed 3,000 individual product-level demand forecasting models across 15 markets — all managed from a single ML Pipeline workspace with automated retraining.

22%

Reduction in inventory waste

Healthcare

Diagnostic AI Model Operations

A hospital group operates 8 clinical decision support models (radiology, pathology, triage) through Mwzn ML Pipeline — with full audit trails, model versioning, and drift monitoring required by regulators.

8 models

In production with full audit trail

[ Integrations ]