MLOps at Shivacha
Practices and tooling for reproducible, monitored, continuously improved models.
Overview
MLOps applies DevOps discipline to machine learning: versioned data and models, reproducible pipelines, automated deployment, monitoring for drift and scheduled retraining. It is what turns a model into a maintainable production system.
Why we use it
- Reproducible training
- Automated deployment
- Drift monitoring
- Governed model lifecycle
How we use it
MLOps in our engineering work
Model pipelines
Training to deployment automation.
Monitoring
Performance and drift tracking.
Governance
Model registry and approvals.
Services
Services that use MLOps
Machine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreCredit Scoring Platform
Credit scoring and decisioning platforms using bureau, banking and alternative data — explainable, monitored models.
Learn moreAI Development
End-to-end AI development: from use-case selection and data preparation to models, applications and production operations.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn morePairs well with
What we combine with MLOps
Models, retrieval, agents and ML operations.
Insights
Related insights
Why most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn more
Build with MLOps.
Tell us about your project, or the engineers you need, and we will propose an approach.