Introduction "AI Services"
Main Goals
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Leverage cross-module usage of data processed in IAS to create insights
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Base for cross domain features with Data and AI focus
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Combination of Data Science and domain knowledge
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Implementation of ML workflows: Training, Scoring, Extraction, Retrain, Optimization
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Enables 3rd party Data Scientists to implement their own Use Cases
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Offers data as a product
Essential Features
Training of AI Models
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Preprocessing of data
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Collecting of data
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Automated training of an individual AI Model to serve the functional use case
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Automated training of an individual AI Model to serve the data quality assessment
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Sorting out bad quality data for training
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Retraining of AI Models
Scoring of data
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Assessment of bad data quality
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Assessment of data provided to serve the functional use case (e.g. anomaly detection, event sequence detection)
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Provisioning of Deviation Notifications