4 Machine Learning Model Testing Criteria That Drive AI Performance

Artificial intelligence (AI) has a quality problem. And it’s big. According to Gartner, 54% of machine learning (ML) models never make it into production, primarily because of poor quality. Sometimes, the models don’t work as intended and developers can’t identify the cause to fix it. Other times, the models work but aren’t explainable, so they can’t be deployed for compliance reasons. Sometimes, even when the model’s results are explainable, stakeholders don’t trust them and they aren’t deployed. There are four important ML model testing criteria and developers need all four to ensure their models make it over the finish line.



In a recent interview, I sat down with Richard Larson, author of the new book “Model Thinking for Everyday Life.” In this Q&A, we delve into the concept of model thinking and its application in our daily lives. Larson shares his insights on the book, what inspired him to write it and why it stands out from other decision-making books in the market. Join us as we explore the fascinating world of model thinking with Richard Larson.

The 2023 INFORMS Business Analytics Conference in Aurora, Colorado, brought together hundreds of leading analytics, AI, ML and data science professionals and industry experts to discover new mathematical solutions to problems, networking strategies for career advancement, and recognized individual and team efforts with the most prestigious awards in analytics and operations research. Here's what you may have missed.

Gordon Moore, co-founder of Intel and giant of the semiconductor and technology industry, passed away on Friday, March 24, 2023, at his home in Hawaii. Moore was 94.

The 2023 UPS George D. Smith Prize was awarded to Purdue University, Mitchell E. Daniels, Jr. School of Business, Business Analytics and Information Management, during the Edelman Gala at the 2023 INFORMS Business Analytics Conference in Aurora, Colorado.

Just-in-Time Logistics: What It Means and Why It Matters

Just-in-time logistics (JIT) is an inventory management system that allows companies to optimize inventory levels, reduce waste and improve efficiency by synchronizing the flow of materials with production and consumer demand. Organizations around the world have adopted JIT, which is particularly effective in the automotive, electronics, retail, pharmaceutical, food and beverage and e-commerce (product fulfillment) industries. One of the most effective ways to unleash the full potential of JIT is with data analytics.



How Using Multiple Data Sources Can Unlock the Power of Network Analytics

Data can be sourced from and stored in different IT systems such as enterprise resource planning, customer relationship management, procurement or the manufacturing system. This has been a traditional approach for operational processes, but when companies want to make the data available for other purposes, including analysis, machine learning, enterprise reporting or fraud detection, it can be challenging, time-consuming and complex. When properly analyzed and sorted, businesses and organizations can use data from heterogeneous sources to drive meaningful insights, anticipate challenges and support proactive or reactive decision-making.


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To Avoid Wasting Money on Artificial Intelligence, Business Leaders Need More AI Acumen

Business leaders need more machine learning acumen to properly translate business objectives into the requirements for a predictive model. Without this, pitfalls abound. In this article, we show why one client’s deployed lead-scoring model is potentially worthless for improving sales team efforts and how the difference between targeting marketing and targeting sales is rarely understood.


How Can Breaking Down Departmental Silos Positively Impact Technology Optimization?

Departmental silos can have a detrimental effect on technology optimization within an organization. Departments may use different technologies or tools, leading to duplication of effort, reduced efficiency and increased costs, which is a common outcome when departments do not communicate efficiently. If departments are not aware of the technologies being used by other departments, they may miss out on opportunities to collaborate and implement new tools or software, causing delays in adopting new technologies, leading to reduced efficiency and productivity.



Why Data Science Projects Fail

why data science projects fail part 5

Part 5: Effective Communication

Clear, concise communication – verbal, written, nonverbal – or lack thereof represents a significant challenge in business in general upon which we all strive to improve. In the field of data science, the communication challenge is even more acute for several reasons, not the least of which is that business people and data scientists rarely speak the same language.

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Benefits of Predictive Analytics for Businesses

Predictive analytics is onboarding a range of business processes across the globe. We list five benefits of predictive analytics that are helping organizations streamline their operations and generate better ROIs.


The Future of Business Analytics: Trends and Predictions

Business analytics is an important tool in many organizations’ business decision-making process. With the rise of big data, the field of business analytics is becoming increasingly advanced with multiple trends and patterns changing. It is important to tread alongside these trends to stay relevant and competitive.



Inside Story

Am I better at my job than a machine?

Summary of what's inside the Winter 2023 issue of Analytics magazine.

Analyze This!

My Biggest Worry about Generative AI

I have come to realize that I’m far less afraid of a mass extinction event caused by sentient AI and far more concerned about these incredibly powerful multiuse tools finding their way into the hands of cybercriminals. What really worries me about generative AI is that it could easily end up arming malicious hackers with a much more powerful set of tools, giving them the ability to disrupt our personal and professional lives in potentially catastrophic ways.

Healthcare Analytics

A Framework for Responsible Artificial Intelligence

The need for responsible AI is necessary in the domain of healthcare, perhaps more so than in any other domain. Techopedia.com defines responsible AI as the “development and use of artificial intelligence in a way that is ethically and socially trustworthy.” Not violating human rights in specific use cases is also part of being responsible, and hence, the development of AI needs to follow that principle as well. AI engines, which are rooted in deep learning and transformer models, are immensely complex and trained with billions of data sets scraped from the internet. Many times, consideration to data privacy and adequate attribution are not part of the output.


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Data-driven Decisions: Is Your Operation Wise?

Organizations are investing heavily in data and analytics to turn data into information and knowledge. Let’s look at how this process generally goes.


Creating a Better Search Experience with Dynamic and Personalized Sponsored Ads

Grocery stores are committed to seamless online shopping – along with the promise of fresh products and incredible value. Data science teams can help deploy technology that enhances the customer experience and helps them fill their baskets faster. To achieve those goals, 84.51° developed an innovative machine learning solution known as Dynamic Positioner that helps increase relevancy and engagement for shoppers.

Five-Minute Analyst

Hot dogs, peanuts, Cracker Jacks and robots

Play ball! A rule change for the 2023 season of Minor League Baseball announced that all Triple-A ballparks must have a robot umpire calling the balls and strikes. There is still need for an in-person umpire because the technology is not perfect and can fail to provide a decision. There has been plenty of commentary from players and coaches about the impacts of this change. Let’s analyze it in five minutes.

Click here for the full blog series Understanding smart technology - and ourselves by Joesph Byrum
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