ARTICLES
The Future of Embeddings for Computer Vision Data Curation
The concept of embedding in machine learning can be traced back to the early 2000s, with the development of techniques such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS). These methods focused on finding low-dimensional representations of...
tinyML Talks: Running and Managing Fleets of Single Board Computers at Scale
Presented by the tinyML Foundation: Running and Managing Fleets of Single Board Computers at Scale. The increase of compute power available on single board computers (SBCs) has opened the door to a whole new class of ML-powered applications that can run on the likes...
How To Fine-Tune Hugging Face Transformers on a Custom Dataset
In this article, we will take a look at some of the Hugging Face Transformers library features, in order to fine-tune our model on a custom dataset. The Hugging Face library provides easy-to-use APIs to download, train, and infer state-of-the-art pre-trained models...
AI Quality Management: Key Processes and Tools, Part 1
Achieving high AI Quality requires the right combination of people, processes, and tools. In the post, “What is AI Quality?” we defined what AI Quality is and how it is key to solving critical challenges facing AI today. That is, AI Quality is the set...
Shelf Engine’s CEO On Disruptive Innovation Without Disruptive Adoption and the AI-Driven Future of Grocery Retail
Stefan Kalb is on a mission to eliminate food waste and revolutionize the grocery business. Shelf Engine – the company he co-founded and leads as CEO – has notched an impressive track record since its founding in 2016, diverting over 4.5 million pounds of food waste...
The Double-Edged Sword of Generative AI: Understanding & Navigating Risks in the Enterprise Realm
Executive Summary Generative AI models and LLMs, while offering significant potential for automating tasks and boosting productivity, present risks such as confidentiality breaches, intellectual property infringement, and data privacy violations that CXOs must...
Feature Engineering for Fraud Detection
Introduction Fraud detection is critical in keeping remediating fraud and services safe and functional. First and foremost, it helps to protect businesses and individuals from financial loss. By identifying potential instances of fraud, companies can take steps to...
Machine Learning at the Edge: Elements Needed for Scale
Learn about the elements you need to build an efficient, scalable edge ML architecture. There are four components that can help bring order to the chaos that is running ML at the edge and allow you to build an efficient, scalable edge ML architecture: Central...
When Machine Learning meets privacy
Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations. This posted has been republished by AIIA. To listen to the original podcast, please click HERE....
Centaur at Work: Writing a Newsletter
A Story of Many Failures and One Success In this blog, join me as I embark on the complex journey of semi-automating an AI-focused weekly newsletter. You'll get a first-hand look at a diverse set of tools and tactics that I've put to work on this real-life puzzle....
Connect with Us
Follow US