Release Notes Oct 2021
Our commitment at Innotescus is to always do what’s best for our customers. As a result, we are always implementing user-oriented solutions on our platform. We owe a great deal
How We Placed Second in Andrew Ng’s Data-Centric AI Challenge
Shashank, Chris, and Rob first worked together at ChemImage Corporation, leveraging machine learning with dense hyperspectral imaging datasets to identify chemical
Mapping Out the Metaverse with Machine Learning
After announcing plans to make Facebook a ‘metaverse company,’ the internet is abuzz trying to figure out what that even means. Plenty of folks have made half-decent
Learning Curve Graphs Part 2: The Experiment
In part 1, we discussed the data requirement curse for non-linear machine learning models. We saw that we can use learning curve graphs to estimate the dataset size for target
Learning Curve Graphs Part 1: Countering the Data Requirement Curse
Broadly speaking, most machine learning algorithms fall into one of two categories: linear models or non-linear models. Linear models are easy to interpret, faster to train and deploy
Synthetic Data: What, Why, and How?
It’s no secret that a comprehensive, well-labeled dataset goes a long way towards an effective Machine Learning solution, and while data collection is a large part of…
Is There 20/20 Vision in Computer Vision?
In this post, we discuss how man and machine can work together in the image annotation process to make computer vision training more robust.
Computer Vision Projects Help Support the Fight Against COVID
In this post, Innotescus spotlights several computer vision/ML projects that support the fight against COVID. including a ML model that interprets chest x-rays.
A Complete Guide to an Iterative Process for Data Cleaning
In this blog, we discuss how you can improve the data cleaning process through iteration and prioritization. You can also download a FREE data cleaning rulebook template.
Reducing Data Bias – ML’s Great Challenge
Arguably, the most important challenge facing ML today involves data science and social equity. In this blog, we discuss how bias can be introduced into ML models…