All content for data scientists and ML engineers

Design Article

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 …

Vladimir

Deploy Development Article

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 …

Vladimir

Monitor Article

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 …

Vladimir

Development Deploy Monitor Article

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 …

Vladimir

Development Deploy Monitor Article

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 …

Vladimir

Development Deploy Article

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 …

Mihail

Design Article

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 …

Vladimir

Design Article

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 …

Vladimir

Development Deploy Monitor Article

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 …

Maulik Chetri

Design Development Guide

The vision AI blueprint

A walkthrough on how to deliver successful vision AI projects

Tobias Schaffrath Rosario

Development Deploy Article

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 …

Alex Wennman

Design Article

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.

Vladimir

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Tuple

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.