Crafting Highly Efficient and Optimal Machine Learning Models for DSPs Using Assisted AIML Framework

Digital Service Providers (DSPs) are implementing machine learning (ML) technologies for various use cases in their ecosystem. Auto Machine Learning (Auto AIML) frameworks enables 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 ML pipeline creation process with assisted data exploratory analysis and modelling 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

  1. Improved transparency – More than two-fold increase in model transparency
  2. Control over modelling 5X improved control over data model process
  3. Enables building optimized and robust model
  4. More flexible in terms of model fine tuning & hyper parameter optimization

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  • Kalpana Angamuthu
  • Mogan A B
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