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Description
Qlik AutoML Features
Qlik AutoML is a no-code, automated machine learning solution designed for analytics teams. It allows users to quickly generate machine learning models, make predictions, and plan decisions.
Key Features
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Auto-generate machine learning models: Qlik AutoML identifies key drivers in your data and generates machine learning models using optimal algorithms. Users can score and rank multiple machine learning models, determine feature importance, refine their models, and deploy models with full explainability data.
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Make predictions: With a predictive model, users can make predictions on their current data and get explainability data at the record-level. This allows users to understand why predictions were made and how to best influence outcomes.
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Explore predictive data and test what-if scenarios: Users can plan for decisions and actions by exploring predictive data and testing “what-if” scenarios in Qlik Sense. Advanced predictive analytics integration provides real-time, instant calculations from AutoML.
Use Cases
Qlik AutoML is applicable in various departments and industries, including:
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Sales: Sales Pipeline – Win / Loss Forecasting, Customer Churn / Retention, Customer Prospecting / Targeting
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Marketing: Demand / Revenue Forecasting, Customer Lifetime Value, Customer Next Best Offer
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Finance: Capital Investment Optimization, Expense Management, Risk Management / Reduction
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Operations: Workforce Demand Prediction, Capacity Allocation, Appointment Cancellations
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HR: Employee Retention / Attrition Prediction, Employee Satisfaction, Recruiting / Candidate Profiling
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IT: Software / Licensing Usage, Infrastructure Performance Prediction
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Supply Chain: Inventory Stock-Outs Prediction, Supply Chain Performance / Bottlenecks, Transportation Optimization
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Service & Support: Support Case Prediction, Predictive Maintenance
Related AI Tools
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Tasks Analytics:*Data analysis Data visualization Business intelligence Predictive analyticsData Integration:*How to integrate data from multiple sources Data fusion Data wrangling Data cleansingData Quality:*Data validation Data cleaning Data enrichment Data governanceMachine Learning:*Machine learning algorithms Model training Model deployment Model evaluation
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