Data Platforms & ML Infrastructure — The Path to Extracting Value from Data
Capivon Data enables you to extract maximum value from your data with modern data architectures, ML infrastructure, and analytics solutions. End-to-end data platform from data lakes to feature stores, ETL pipelines to MLOps.
We empower your data-driven decision-making processes with production-ready ML systems and scalable data architectures.
Data lake, data warehouse, and lakehouse architectures. Data mesh implementation, medallion architecture (bronze/silver/gold layers). Real-time and batch processing infrastructure.
Automated data collection and transformation pipelines. Stream processing, data quality validation, schema evolution. Orchestration and scheduling.
Feature store, model registry, experiment tracking. Model training infrastructure, automated retraining pipelines. Model serving, A/B testing, and monitoring.
Self-service analytics infrastructure, metrics layer creation. Dashboard and reporting systems. Data catalog and documentation.
Data lineage tracking, data quality framework, automated testing. Privacy compliance (GDPR, KVKK), data access controls, and audit logging.
Event streaming platforms, real-time processing, CDC (Change Data Capture). Stream-batch unification, low-latency analytics.
Real-time feature engineering, model serving infrastructure, A/B testing framework for personalized recommendations.
Automated ML pipelines and production deployment for churn prediction, demand forecasting, and anomaly detection.
Unified customer view from multiple data sources, behavioral analytics, and segmentation.
Self-service analytics, automated reporting, real-time dashboards for data-driven decision making.
Real-time transaction analysis, ML-based anomaly detection, automated alert systems.
Semantic search, vector databases, ranking models for advanced search capabilities.
Collection from data sources, cleaning, validation, and feature engineering. Data versioning and reproducibility.
Experiment tracking, hyperparameter tuning, distributed training. Model evaluation and comparison.
Production deployment, auto-scaling, low-latency serving. Canary releases and shadow deployments.
Model performance monitoring, data drift detection, automated retraining. Feature store updates and model versioning.
Recommendation engines, inventory optimization, customer analytics
Fraud detection, risk modeling, customer lifetime value prediction
Product analytics, user behavior modeling, churn prediction
Data warehouse modernization, BI platform upgrade, data governance
Schedule a consultation for data maturity assessment and roadmap creation
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