Explainable ML Model to Achieve 5X Transparency and Control in AIOps
Digital Service Providers (DSPs) are implementing machine learning (ML) technologies for various use cases in their ecosystem. Auto Machine Learning (Auto AIML) frameworks enable DSPs in various Digital transformation programs. However, these widely available Auto AIML frameworks like TPOT, Auto-Sklearn, AutoKeras, H2O Driverless AI, Microsoft AutoML, Google AutoML are black-box models that provide a generic machine learning pipeline and don’t enable domain characterization and flexibility for the data scientists to analyze business-specific data.
Figure1: Assisted Artificial Intelligence and Machine Learning Framework
This insight elaborates on the Assisted Artificial Intelligence and Machine Learning (AIML) framework, which provides greater transparency and flexibility in building machine learning models by incorporating domain characterization. This framework plays an important role in every phase of the ML pipeline creation process with assisted data exploratory analysis and modeling techniques, enabling DSPs to build highly robust, scalable, and optimal models for various business problems.
Expected benefits that could be leveraged over Auto AIML frameworks are
- Improved transparency – More than two-fold increase in model transparency
- Control over modeling – 5X improved control over the data model process
- Enables building an optimized and robust model
- More flexible in terms of model fine-tuning & hyperparameter optimization
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- Kalpana Angamuthu
- Mogan A B