Trustworthy AI in manufacturing
Explain, analyze, and monitor ML models.
Improve quality. Build trust. Scale up.
Achieving Business Success with AI in manufacturing is challenging
Artificial Intelligence/ Machine Learning (AI/ML) has the potential to have far reaching impacts on Manufacturing. However, in most manufacturers, scaling adoption and achieving business success is hard.
Key challenges to achieving the promise of AI/ML include establishing and sustaining the quality, performance and trustworthiness of AI/ML models.
Veritable’s AI Quality platform helps explain, test, monitor and debug AI/ML models – to ensure quality, build trust and enable adoption at scale.
Explainability
Error Analysis
Was the data set used to train the customer authentication system representative of the population? Will the customer risk model trained for one segment work with another?
Monitoring
Are women less likely to get a loan? What is driving the disparity with men? Can it be rectified? And how?
Stability
How did the credit model react to Covid 19? Which factors drove the change? Is the model still fit for use? How is it likely to react to future changes?
Veritable helps manufacturers capture real business value from AI and ML at scale
Faster deployment
Better quality models, earlier in the model lifecycle. Shorter validation timelines
Greater buy-in
Easier for impacted stakeholders (staff, business leaders, customers) to understand and trust the models
Robust governance
Automated compliance to evolving regulatory expectations and internal model and data standards
AI/ML models for manufacturing need to be explained, analysed and monitored
Forecasting
- Help address challenges around performance & overfitting across segments such as low volume products
- Provide transparency to and foster collaboration with demand forecasting stakeholders
- Efficiently assess and debug ML model drift & operational performance
Predictive Maintenance
- Support iterative testing & improvement to build high performance models overall & across important segments
- Explain predictions to operators to take next best action, built trust
- Debug false positives and false negatives
- Monitor and debug ML model drift
Predictive Quality
(Smart Manufacturing)
- Support building high performance models overall & across important segments
- Explain predictions to operators to take next best action, gain insights & built trust
- Debug false positives and false negatives
- Monitor and debug data quality issue and ML model drift
Anomaly Detection
- Explain why model is predicting an anomaly to improve model and achieve stakeholder buy-in
- Monitor the effectiveness and stability of the model on an ongoing basis
Warehouse Management
- Aid in model selection & model performance optimization
- Explain predictions to operators to take next best action, enable collaboration & built trust
- Monitor and debug data quality issues and ML model drift
Operations Automation
- Provide early warning when data drift is likely to impact the accuracy of models used to automate operational processes
- Assess the reliability of the model to determine the appropriate level of human supervision
Why use Veritable for manufacturing?
Model quality through the lifecycle: development evaluation & testing and production monitoring
Faster and more accurate explainability than off the shelf open source
Broad support for different ML model/ data types and platforms
Easily embedded into Manufacturing tech stacks
Expertise and validation with Manufacturing customers
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