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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 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 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
