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Quantum theory provides an unvarying framework for the construction of various modern physical theories. After more than 50 years from its inception, quantum theory married with computer science, another great intellectual triumph of the 20th century and the new subject of quantum computation was born. One of the fertile areas for quantum computing is AI (Artificial Intelligence), which relies on processing huge amounts of complex datasets. There is also a need to evolve algorithms to allow for better learning, reasoning and understanding.

This talk underlines the necessity of a custom designed recommender system for a business case comprising of several complex underlying business rules & intertwined ways of working. It will help the audience to think out of the box when off-the-shelf machine learning algorithms don’t completely solve a business problem & warrant a custom-made solution. The services unit of Ericsson wanted to have a recommender system for their resource manager who routes an incoming project demand to an appropriate pool of resources today, based on his experience. The demand routing is considered successful only if the routed resource pool has the expected resources available who can support the delivery of the project. The standard algorithms like apriori, FP-growth etc. were not suitable for this problem because each recommendation needed to factor in the complex feature set of the input project demand & recommend out of a set of 700 pools. The input demand feature set was a mixture of several categorical, time based, text & few numeric features. The problem complexity was further compounded by the need to consider the composition of the resource pools before recommending the suitable resource pools. The AI & Data Innovation team devised a customized Machine Learning solution which was a combination of innovative feature engineering, clustering techniques, classification algorithms & usage of real time resource availability data via APIs. The automated offline training of the model using an Azure Databricks pipeline enables it for quick adaptability. The E2E machine learning solution was implemented as a web service using MLflow, Azure Kubernetes Services. The Machine Learning service’s enhancements are handled by the Azure CICD pipeline. The Machine Learning service also has a 3-tier support structure with Data Scientists at L3.