Challenges We Solve
In retail & e-commerce, using historical data, ML algorithms and models can predict the number of products, cost, demand & mores. Centralization and processing of data from the entire supply chain reduces overheads and increases profits.
With collation of relevant customer data from various sources, a media or retail company can make an accurate prediction for providing personalized marketing offers and incentives to reduce churn and increase the customer lifetime value.
In Manufacturing & Automobile industries, a very critical requirement is to identify and prevent deviations from standards in quality. With image analysis, anomaly detection and reinforced learning, ML can help reduce defect rates.
With continuous data from supply-chain and process pipelines, Machine Learning can be trained to look for deviations, anomalies and also suggest historically best combinations for an efficient process.
How We Help
Data Preparation, Preview & Pilot
- Data Exploration and initial assessment of datasets
- Preparation, validation and transformation of data feed
- Previews, pilots and PoC’s of ML models
Manage ML platforms & Infrastructure
- Deploy and deliver ML engine on cloud platforms
- Monitor, tune and optimize ML engines on cloud platforms
- Finalization and operation of Production ML
MLOps & Observabilty
- Operations & production support for ML platforms
- Managed services for optimized, efficient ML Operations
- Enhanced logging, and observability engineering for ML Ops