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How to Organize Data Labeling for Machine Learning: Practical Approaches

In this contributed article, AI and computer vision enthusiast Melanie Johnson discusses how prior organization of data labeling for a machine learning project is key to success. Organizing data labeling for machine learning is not a one sitting job, yet a single error by a data labeler may cost you a fortune. Now, you probably wonder how do I get high-quality datasets without investing so much time and money?

Redistricting with Optimization

This contributed article discusses how optimization is the most transparent and fair method of creating political districts. However, optimization is a highly challenging process that seeks the ideal answer to a problem with hundreds of millions of possible solutions.  The enormity of the problem can be addressed in 2021 because states like Michigan and Virginia are now seriously addressing the gerrymandering issue, while advances in computer software and hardware have made the necessary large-scale optimization possible.

Yes, Data is Valuable—But Much of the Time, It’s Better to Hit Delete

In this special guest feature, Bill Tolson, VP of Global Compliance & eDiscovery at Archive360, discusses the big question surrounding Big Data: when (and how) can information be legally deleted? What’s needed is the right combination of technology and policy.

The Future Is Now: Why Data Is Key to Tech Research & Development

In this contributed article, IT and digital marketing specialist Natasha Lane, highlights the reasons why using data is so crucial for research and development. Using data can be the key to recognizing and solving humanity’s leading challenges in the years to come. We’re talking about everything from water shortage, climate change, the need to develop safe self-driving cars, and so on.

5 Misconceptions of ML Observability

In this special guest feature, Aparna Dhinakaran, Chief Product Officer at Arize AI, explains five of the biggest misconceptions surrounding machine learning observability. As tools emerge to facilitate the three stages of the machine learning workflow–data preparation, model building, and production–it’s typical for teams to develop misconceptions as they attempt to make sense of the crowded, confusing, and complex ML Infrastructure space.

Preparing Healthcare Data for the Netflix Effect

In this contributed article, Chris Luoma, Senior Vice President of Global Product Management at Global Healthcare Exchange (GHX), discusses how to achieve the Netflix Effect in healthcare with four best practices­­. The “Netflix Effect” is the uncanny ability of brands to know exactly what we want, when we want it.

Mathematical Optimization: A Powerful Prescriptive Analytics Technology That Belongs In Your Data Science Toolbox

In this special guest feature, Dr. Gregory Glockner, Vice President and Technical Fellow at Gurobi, explains how you can get started using mathematical optimization and provides some examples of how this prescriptive analytics technology can be combined with machine learning to deliver business benefits across various industries.

How AI is Revolutionizing the Freight Industry

In this contributed article, Oleg Yanchyk, CIO of Sleek Technologies, discusses how AI is shaking up the logistics space and what could lay on the horizon. From making business intelligence decisions more agile to ensuring data is clean and ready to be analyzed, AI is fundamentally changing how shippers and carriers process, synthesize and act on their data.

Why It’s Time to Embrace Data Lakes

In this special guest feature, Craig Kelly, VP of Analytics at Syntax, discusses how data lakes can help companies better analyze and use the mounds of data they already store. Data lake technology is helping cutting-edge organizations take control and generate value from their data.

Why You Need a Data Strategy for Your Customer’s Embedded Analytics

In this contributed article, Charles Caldwell,VP of Product Management at Logi Analytics, discusses how embedded analytics has become more than a “nice to have” feature for an application. It’s an end-user expectation. But meeting that expectation requires far more than meeting the initial task at hand. As your data architecture evolves, you will need to ensure your solution meets your customer’s needs as well as the performance expectations of the customer’s users.