$ ./solutions/mlops

MLOps_

Getting a model into production is where most AI initiatives stall. Automation and operational discipline across the full machine learning lifecycle, inside the AI Factory and beyond, keep models shipping reliably at any stage of MLOps maturity.

// EVERY MODEL, ON RAILS
notebookexperiment #214
train4x A100 · 38 min
registryv2.3 staged ✓
deploycanary 10%
monitordrift: watching
⚠ drift detected on feature "tenure"→ auto-retrain queued

What's in scope

// BUILD

ML Pipeline Automation

Turn training, validation, and deployment steps into reproducible, automated pipelines.

Experiment Tracking & Registry

Version datasets, experiments, and models for full lineage and reproducibility.

Feature Stores

Centralized, consistent feature pipelines shared across training and serving.

// SHIP

CI/CD for Models

Continuous integration and delivery pipelines extended to data, models, and code.

Model Deployment & Serving

Package and serve models as scalable, containerized inference endpoints.

Containers & Orchestration

Kubernetes-based orchestration for training and inference across cloud and on-prem.

Cloud-Native Applications

Microservices and APIs that wrap models into differentiated products.

Modernizing Traditional Apps

Enhance legacy systems with improved portability, efficiency, and security, without restructuring.

// RUN

Model Monitoring & Drift

Detect data drift, model decay, and performance regressions in production.

Governance & Compliance

Automated testing, audit trails, and policy enforcement for responsible AI.

Infrastructure Automation

Turn GPU and cluster operations into code across cloud, VM, and container estates.

MLOps Expertise

Support for organizations at any stage of MLOps maturity.

Have a project in mind?_

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