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.
Feb 7, 2024
Celebrate iMerit's 10-year journey with Radha Basu, leading a revolution in responsible AI and inclusive workforce. From dream to reality, join the global impact of a technology-driven AI Data Solutions company.
Dec 27, 2022
Automation in machine learning will enable people to make a bigger impact and have more fulfilling roles.
Dec 13, 2022
Find out how these leading companies deploy robust ML systems in production by converging ML Ops and Data Pipelines.
Dec 7, 2022
Find out how comprehensive data annotation tools can help overcome the six most common barriers for high-quality data.
Dec 1, 2022
Responsible AI is a key investment to help AI adoption and instill trust in end-users.
Nov 24, 2022
iMerit CEO Radha Basu and Meta’s Manohar Paluri discuss the ideal framework for pushing the frontiers in AI research at the ML DataOps Summit.
Nov 11, 2022
Here’s how machine learning can help categorize text-based documents using supervised classification algorithms.
Nov 7, 2022
Learn how and why DNNs are vulnerable to adversarial attacks, and how to make machine learning models less vulnerable to these attacks.
Oct 27, 2022
As videos have become easier to distribute and consume, we have seen considerable developments in machine learning algorithms that can
Oct 20, 2022
Learn about the basics of image classification with this tutorial on convolutional neural networks with Tensorflow. A sample script is included.
Oct 12, 2022
Learn the steps involved in developing a machine learning model and the distinction between supervised and unsupervised learning.
Oct 7, 2022
Learn about the role of data annotation in enabling AI, the different approaches to data annotation, and the outlook for data annotation companies in 2022.
Sep 23, 2022
This post is intended to serve as a guide for those data scientists who want to ensure their supervised learning algorithm avoids common ML pitfalls.