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How to Compare ML Solutions Effectively
Increasing the chances of getting a model to production When evaluating and comparing machine learning solutions, your first go-to evaluation metric will probably be predictive power. It’s easy to compare different models with one single metric, and this is perfectly fine in Kaggle competitions. In real life, the situation is
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How Misused Terminology Is Damaging the Data Field
What is the difference between “machine learning” and “artificial intelligence”? What about the difference between “data science” and “data analytics”? And what is this new field “business science” all about? Terminology in data fields is a mess, causing chaos for both employers and job seekers. Imagine applying for a “Data
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The Jellyfish and the Flatworm: A Story About AI Strategy
Many executives are pondering difficult decisions about making large investments in AI. For many of them, their lack of a technical background makes it difficult for them to visualize the impact of AI on their customers, their products, and their employees. To help executives make the right strategic decisions, we
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From Programming to Managing Managers: 5 Things I Learned the Hard Way
Leaders don’t become great because of their power, but because of their ability to empower others. When I moved from being an individual contributor (IC) to a manager, my responsibilities shifted from doing work to getting things done through others. My job was no longer about the number of lines
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IIA names Charles Schwab as recipient of 2023 ANNY Award for Excellence in Analytics
Portland, Ore. (Oct. 25, 2023) – The International Institute for Analytics (IIA), the leading independent analytics and data science research and advisory firm, named Charles Schwab the winner of the 2023 Excellence in Analytics Award at IIA’s 2023 ANNY Award Showcase held Thursday, October 24, 2023. All applicants competed on
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Cultural Competencies for Machine Learning Risk Management
An organization's culture is an essential aspect of responsible AI. A Note on the Series: As we embark on this series, it’s important to provide context. I am one of the co-authors of Machine Learning for High-Risk Applications, along with Patrick Hall and James Curtis. This series is designed to
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Organizational Processes for Machine Learning Risk Management
Organizational processes are a key nontechnical determinant of reliability in ML systems. In our ongoing series on machine learning risk management, we've embarked on a journey to unravel the critical elements that ensure the trustworthiness of machine learning (ML) systems. In our first installment, we delved into “Cultural Competencies for
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October: Best of the Web
Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include a blog on ways to curb heavy data center consumption, key takeaways from Biden's executive order on AI legislation, and a piece utilizing AI
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Bridging Domains: Infusing Financial, Privacy, and Software Best Practices into ML Risk Management
Understanding strategies that go beyond traditional Model Risk Management “Aviation laws were written in blood. Let’s not reproduce that methodology with AI” — Siméon Campos In 2018, Bloomberg’s story "Zillow's Algorithm-Fueled Buying Spree Doomed Its Home-Flipping Experiment" made quite a headline. It outlined Zillow's daring entry into the iBuying world,
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We Got It Wrong: Data Isn’t About Decision-Making
We data people have got it all wrong for decades. We think we do this data stuff to support decision-making. We build data lakes and platforms to enable business to “make informed decisions.” We develop highly visual and interactive dashboards to allow people to analyze data and “make data-driven decisions.”