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Scarecrow: ML for Aircraft Engine Management

Project done at Deloitte (Client: Rolls-Royce)

Technologies used: Python, streaming machine learning, Data bricks Timeline : Feb 2021 - Current

Publications: Presented Scarecrow - Intelligent Annotation platform for Engine Health Management in AI ML Systems conference

White papers: Demonstrating online learning on Rolls-Royce blogs

Impact: Preventive maintenance identified with 15% less false positivity(estimated)

Team: 7-10 member team consisting of data engineers and data scientists

Problem Statement: Assisting subject-matter experts (SME) in identifying various performance issues in an engine

Built a machine learning framework that continuously learns (streaming machine learning) by observing the decisions taken by SME's on a Tableau dashboard. SME's look at data from different sensors from aircraft engines in flight to identify the engines\parts that may need maintenance or have low performance. These models are used to reduce the amount of data that SME's should review by identifying only the less confidence predictions (active learning).

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