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Democratization of AI/ML: Machine Learning for the Masses

May 2019 Presentation
Ryan Luckay (Deloitte Consulting LLP), Tracy Bannon (Deloitte Consulting LLP)

This presentation demonstrates several software tools that make using artificial intelligence and machine learning easier for non-data scientists.

Publisher:

Software Engineering Institute

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Abstract

The opportunity for artificial intelligence (AI) and machine learning (ML) is huge and could contribute $13 billion to the global economy by 2030. Six companies have collectedly invested over $7.7 billion in AI/ML research and development: Google, Amazon, Apple, Intel, Microsoft, and Uber. However, according to the August 2018 LinkedIn Workforce Report, data science skills are in short supply. IBM predicts that by 2020, the number of jobs for all U.S. data professionals will increase by 364,000 openings.

Tools make it easier for non-data scientists to do AI/ML. Common ML use cases include customer lifetime value modeling, churn modeling, dynamic pricing, customer segmentation, image classification, and recommendation engines. ML APIs, Keras, and AutoML enable addressing these turnkey ML use cases. This talk also introduces a use case for autonomous inspections of aircraft. The market opportunity for infrastructure inspection is over $45.2 billion, with predictive maintenance growing 400% by 2022. Similar solutions have seen the following results: 10x reduction in time spent in inspections, 20–30% reduction in maintenance costs, and 15–20% reduction in unplanned downtime.

This session will demonstrate the following Google tools: Pub/Sub, Cloud Storage, Machine Learning APIs/AutoML, App Engine, Google Street View API, and Google Earth API.