Seldon for the Manufacturing Industry
Enabling manufacturing businesses to make data-driven decisions in real-time
Improving Operational Efficiency
Seldon empowers manufacturing businesses to streamline to streamline their operations, improve the quality of their products, and better serve their customers. Seldon can scale to meet the needs of even the busiest manufacturing businesses, and provides detailed monitoring and management capabilities, which can help you ensure your ML pipelines are running smoothly.
Seldon is the Manufacturing Industry leaders' choice
Why use Seldon in the Manufacturing Industry?
- Supply chain optimization through serving and model management
- Advanced monitoring for enhanced maintenance and quality assurance
- Explainable forecasting models that maintaining high prediction accuracy
“Rising demand from manufacturing industries such as automotive, semiconductors, and medical devices, among others, will contribute significantly towards the growth of artificial intelligence in the manufacturing market. AI helps boost the production process and offer the best quality results to the manufacturer.
Maximize Equipment Efficiency
Effective serving and model management makes it easy to run models at scale while having control over resource usage for dev and production environments. Predictive maintenance models can be deployed to predict equipment failures and schedule maintenance tasks before any issues arise, reducing downtime and maintenance costs.
Real-time Insight into Quality Control
Advanced monitoring and observability provide real-time insight into the performance of machine learning models in the manufacturing industry. Quality control models can be monitored to ensure the accuracy of predictions and detect anomalies in the production process which allows for quick and efficient corrective action.
Gain Deeper Understanding of your Machine Learning Models
Seldon’s explainability features enable manufacturers to understand the reasoning behind the machine learning models’ predictions. Supply chain optimization models help manufacturers gain a deeper understanding of how factors like demand forecasting and inventory levels impact overall costs and make data-driven decisions accordingly.