Applying technology oversight and domain insights in AI and ML initiatives to increase success
More than 85% of data science projects fail. This high failure rate is a main reason why data science is still a “science.” As data science practitioners, reducing this failure rate and improving teams’ confidence in executing successful data science projects is a priority.
Jike Chong and Yue Cathy Chang explain the three key steps of applying data science technology to business problems: scenario mapping, pattern discovery, and success evaluation. And they outline three areas of concerns for applying domain insights in AI and ML initiatives: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.
Scenario mapping involves a taxonomy of analysis types to select the appropriate technology stack for scenarios at hand and techniques to define more flexible analysis and modeling targets to allow for adjustments and tuning to increase project success rate and achieve higher ROI. Pattern discovery includes feature extraction practices to increase vigilance on data characteristics, innovate in feature engineering, and apply early assessments to increase model stability. Jike and Cathy also discern modeling assumptions that are momentum based, foundational, or reflexive. Success evaluation requires assessing different degrees of model confidence required for success, how to flexibly achieve it using multitiered architectures, and how to select an appropriate model operating point to communicate project success.
To clarify business context, you’ll discover the importance of understanding organizational vision and mission; the maturity stage of the organization, product, and feature; and techniques for crystalizing a project’s description, meaning, relevance, value, and purpose. To be aware of the nuances of data sources, you’ll understand the biases, inaccuracies, and incompleteness that inevitability exists in data, and see examples in web session data, geolocation data, and financial transaction data. To navigate organizational structures in specific industries, you’ll interpret the stage of product development, the maturity of the data science infrastructure, and examples for being flexible to adapt to industry norms.
By applying data science technologies to business problems and applying domain insights in AI and ML initiatives, you can reduce the failure rate and improve teams’ confidence in executing successful AI projects.
- Experience with data science projects and initiatives
Materials or downloads needed in advance
- A laptop (useful but not required)
What you'll learn
- Identify the three key steps of applying data science technology to business problems and the three areas of concerns for applying domain insights in AI and ML initiatives
Jike Chong is the director of data science, hiring marketplace at LinkedIn. He’s an accomplished executive and professor with experience across industry and academia. Previously, he was the chief data scientist at Acorns, the leading microinvestment app in US with over four million verified investors, which uses behavioral economics to help the up-and-coming save and invest for a better financial future; was the chief data scientist at Yirendai, an online P2P lending platform with more than $7B loans originated and the first of its kind from China to go public on NYSE; established and headed the data science division at SimplyHired, a leading job search engine in Silicon Valley; advised the Obama administration on using AI to reduce unemployment; and led quantitative risk analytics at Silver Lake Kraftwerk, where he was responsible for applying big data techniques to risk analysis of venture investment. Jike is also an adjunct professor and PhD advisor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where he established the CUDA Research Center and CUDATeaching Center, which focus on the application of GPUs for machine learning. Recently, he developed and taught a new graduate level course on machine learning for Internet finance at Tsinghua University in Beijing, China, where he is serving as an adjunct professor. Jike holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University and a PhD from the University of California, Berkeley. He holds 11 patents (six granted, five pending).
Yue Cathy Chang
Yue “Cathy” Chang is a partner at TutumGene, a technology company that aims to accelerate disease curing by providing solutions for gene therapy and regulation of gene expression. She’s a business executive recognized for sales, business development, and product marketing in high technology. Previously, she was with Silicon Valley Data Science, a startup (acquired by Apple) that provided business transformation consulting to enterprises and other organizations using data science- and engineering-based solutions; employee #1 hired by the CEO at venture-funded software startup Rocana (acquired by Splunk), where she served as senior director of business development focusing on building and growing long-term relationships, and notably increased sales leads 2x through building and managing indirect revenue channels; held multiple strategic roles at blue chip software enterprise companies as well as startups, including corporate and business development at Feedzai and Datameer; senior product management, product marketing and sales at Symantec and IBM; and strategic sourcing improvement consulting at Honeywell. Cathy holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University, MBA and MS degrees as a leaders for global operations (LGO) duel-degree fellow from MIT, and two patents for her early work in microprocessor logic design.
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