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
No-Code Model Building Keeping You In Full Control
We are now launching Model Playground, a model experimentation and building environment where you can train and benchmark models on your data without …
The modern AI workforce part 2: In-house or outsource?
A walkthrough on the costs, benefits, and risks of outsourcing annotation work. We also give you our recommended approach.
Hasty.ai helped us improve our ML workflow by 40%, which is fantastic. It reduced our overall investment by 90% to get high-quality annotations and an initial model.