The iMerit Blog

From casual to cultured, the iMerit blog tackles a wide array of topics related to security, expertise, and flexibility in the Artificial Intelligence and Machine Learning data-enrichment marketplace.

Sep 24, 2021

Looking To Maximize the Potential of Your Dataset?

There’s something we need to get off our chest, and it isn’t easy for us to say: optimizing datasets can

Sep 24, 2021

How to Navigate Data Labeling Solutions for NLP

Navigating data preparation for natural language processing (NLP) applications comes with its fair share of complexity. With such a wide

Sep 22, 2021

The Lazy Data Scientist’s Guide to AI/ML Troubleshooting

Data isn’t born for AI/ML models. While quantifiable, data is still a chaotic mess that needs deciphering before it can

Sep 20, 2021

26 AI/ML Datasets & Search Engines You Can Use Right Now

Looking for some AI/ML datasets? You’ve come to the right place. In an effort to facilitate your search for ground

Sep 17, 2021

Crowdsourced Data Labeling: When To Use it, and When Not To

It’s a fact of life: machine learning and deep learning, while revolutionary, require tremendous volumes of data. Even with algorithmic

Sep 16, 2021

iMerit CEO Radha Basu Speaks To AI For Good: Key Takeaways

Radha Basu, the CEO and founder of iMerit, sits down with Lindsey Asis, Programme coordinator at AI for Good Foundation,

Sep 15, 2021

This is how you know it’s time to bring in professional data labelers

It’s perfectly normal to reassess if your data-labeling methods are meeting the needs of your organization. For anyone who has

Sep 9, 2021

AI on the Fly with Brett Hallinan: Trillion Dollar Infrastructure Bill’s Impact on New Mobility

Join AI on the Fly with Brett Hallinan and autonomous vehicle industry expert Chris Barker as they dive into the

Sep 1, 2021

Four Edge Cases Solved with Out-of-the-Box Thinking

One thing that is certain when it comes to edge cases: most occur within the last mile. As the application

Sep 1, 2021

Data Labeling Pros and Cons: In-House, Crowdsourcing, and Service Providers

If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine

Aug 24, 2021

Natural Language Processing with Words and Pictures

In a previous blog we talked about how early Natural Language Processing (NLP) systems tried to represent word meaning, but

Jul 29, 2021

Top Takeaways from ODSC session: ‘Why ML Projects Fail and How to Avoid Them’

AI and ML are disrupting every industry and companies are investing heavily in hopes of gaining any kind of competitive