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.

Nov 24, 2021

iMerit on the AI in Business Podcast: Key Takeaways

When it comes to the realities of AI adoption and deployment, it’s often the service providers  – executing the hands-on

Nov 19, 2021

Labels Matter: Key Takeaways from The MapScaping Podcast

iMerit Solutions Architect Mallory Dodd is featured on the MapScaping Podcast, “Labels Matter,” hosted by Daniel O’Donohue. She discusses how

Nov 5, 2021

Three Common Machine Learning Pitfalls and How to Avoid Them

There’s no need to reinvent the wheel. While this cliche is often overused, it especially applies to the world of

Oct 26, 2021

Data on the Edge with Datasaur: Key Takeaways

In the inaugural episode of Data on the Edge, Jeff Mills, Chief Revenue Officer of iMerit, sits down with Ivan

Oct 22, 2021

3 Things To Do Before Building Your ML Model

Machine Learning: Prior to Model Building In the world of machine learning, much focus is devoted to the nature of

Oct 12, 2021

iMerit Named in the Top 20 Computer Vision AI Startups to Watch in 2021

iMerit is one of the top 20 computer vision AI startups to watch in 2021, according to a list published

Oct 12, 2021

iMerit Recognized Among 100 Most Innovative Image Recognition Companies in California

iMerit was ranked in Data Magazine’s list of the “100 Most Innovative Image Recognition Companies in California”. The companies were

Oct 8, 2021

Working on an AI Project? Here’s How Much Data You’ll Need.

Considering that 80% of the work required for an AI project is collecting and preparing data, determining how much data

Sep 29, 2021

Learning Common Sense from Video

Today a big gap between human and machine intelligence is common sense. When we humans interpret language or visual scenes,

Sep 24, 2021

Building and Labeling High-Functioning Datasets for Your Model

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