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 …
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 …
Exploration of different Deep Learning model formats
There are many different ML frameworks and, as a consequence, ML formats today. In this article, we summarize popular formats in existence today and …
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 …
Using the Hasty Inference Engine API
As part of our data flywheel , you can get inference results from the model you trained using the Hasty API . We provide a way to upload an image and …
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 …
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%.