Whatever you are paying for labeling is too much
As the first company globally, we are making all labeling features completely free to use, including our best-in-class automation features. Our goal …
How we cleaned up PASCAL and improved mAP by 13%
We cleaned up all 17.120 images of the PASCAL VOC 2012 dataset in a week using Hasty’s AI-powered QC feature. We found that 6.5% of the images in …
Bringing active learning, model explainability, and Bayesian networks to Hasty with the help of ProFit
How we plan to use active learning further to improve the automation and effectiveness of machine learning tasks.
Buy or build ML solutions
This article will look at one of the most complex decisions for most organizations starting new AI projects. Should they buy or build the software …
How to analyze the performance of vision AI models and connect it to business metrics
Many companies want to use AI to improve specific business metrics. But how do you understand the relationship between ML metrics and business …
How to organize ML teams
How do you best structure ML teams? In this article, we break down different approaches and go through the pros and cons of these approaches. …
Implementing the data flywheel
As the ML space is maturing, processes and best practices for what happens after you successfully manage a first project launch are becoming more …
Introduction to MLOps
This introduction to MLOps is intended as an introduction to the field, it's similarities and differences compared with DevOps, and how it can help …
Pricing a vision AI project part 1 : What projects are worth spending money on?
This article, the first of a four-part series, goes through the initial steps needed when getting started with AI. Specifically, we look at how to …
Pricing a vision AI project part 2: Understanding the costs
In our previous article in the series, we looked at how you can prioritize AI projects and gave a quick back-of-the-napkin calculation to use for …
The Frankensuite problem in vision AI
Today, there are many tools, software, and platforms that are aiming to assist AI teams in various ways. Many of them are great. However, an ML …
The vision AI blueprint
A walkthrough on how to deliver successful vision AI projects
Tobias Schaffrath Rosario
Before discovering Hasty, labeling images was labor intensive, time-consuming, and less accurate. Hasty’s approach of training the model while labeling with faster annotate-test cycles has saved Audere countless hours.
Only 13% of vision AI projects make it to production, with Hasty we boost that number to 100%.