In the first part of our guide we focused on properly executing the entire process of building and implementing machine learning models with a focus on the main goal – solving the overarching business challenge. In the second part of our material we dig deeper into the topic of modeling.
In this post, the self-supervised learning paradigm is discussed. This method of training machine learning models is emerging nowadays, especially for high-dimensional data. In order to focus the attention of this article, we will only work on examples from the computer vision area. However, the methods presented are general and may be successfully used for problems from other domains as well.
In this post, we will sum up the very recent history of solving the text-to-image generation problem and explain the latest developments regarding diffusion models, which are playing a huge role in the new, state-of-the-art architectures.
Customized AI software development is one of the most powerful approaches to leveraging AI in business. Undoubtedly, the main difficulty of this approach is the ability to successfully develop and implement AI projects. Cooperation with an experienced AI vendor, who is responsible for end-to-end delivery, is one of the most effective solutions, bringing with it a number of benefits.
Graph usage in AI recently became quite evident with an increased number of research papers and some impressive examples among the industry . This article aims to answer the question: Are there ways to improve a project’s delivery by using graphs even before reaching GraphML?
Much has been said about the effective running of machine learning projects. However, the topic keeps coming up. It is vital to remember that the purpose of ML projects is not modeling itself, but achieving defined business goals.
While enterprises recognize the measurable business benefits of AI adoption, they don’t necessarily see the path to get there. As the Everest Group survey indicates, 3 out of 5 of enterprises fail to adopt AI and don’t achieve meaningful business outcomes. Let’s look for the missing key to harness the full potential of AI implementation.
In recent years, we have seen rapid development in the field of artificial intelligence, which has led to increased interest in areas that have not often been previously addressed. As AI becomes more and more advanced, beyond model effectiveness, experts are being challenged to understand and retrace how the algorithms came up with their results, and how the models are reasoning and why [Samek and Muller, 2019].
We discuss the main challenges of AI implementations in retail and manufacturing with Ireneusz Prus, Director of Data Monetization at Maspex Group.
AI projects requires building interdisciplinary teams – both from business and technology. Executing the process successfully is quite a challenge and hiring and AI vendor may be an effective solution.