AI/ML

Most machine learning projects start off as a single-threaded proof of concept where each task is completed before the next task can begin. The single-threaded ML pipeline depicted below is an example. However, at some point, you will outgrow the pipeline shown above. This may be caused by datasets that no longer fit into the memory of a single process.

The calendar year 2023 will be a meaningful one, perhaps one of the most meaningful ones, when the history of AI is written. It was, in essence, the big bang.  It started in late 2022 with OpenAI’s ChatGPT but it was the response that was so breathtaking. Within months we had Meta’s LLaMA 2, Google’s Bard chatbot

Introduction Distributed data processing is a key component of an efficient end-to-end distributed machine-learning training pipeline. This is true if you are building a basic neural network for statistical predictions where distributed training could mean each experiment runs in 10 minutes vs. an hour. It is also true if you are training or fine-tuning a Large Language Model (LLM) where

This post was written in collaboration with Amit Kesarwani from lakeFS. The reality of running multiple machine learning experiments is that managing them can become unpredictable and complicated - especially in a team environment. What often happens is that during the research process, teams constantly change configuration and data between experiments. For example, try several training sets and several hyperparameter

Introduction Generative AI represents the latest technique an enterprise can employ to unlock the data trapped within its boundaries. The easiest way to conceptualize what is possible with Generative AI is to imagine a customized Large Language Model - similar to the one powering ChatGPT - running inside your firewall. Now, this custom LLM is not the same as the

A lot of ink has been spilled on the significance of the AI/ML technology wave (here are our posts). What doesn’t get attention, but probably should, is how AI/ML is remaking the technology power structure inside the enterprise. As companies reorganize around a data-centric orientation, they are also reorganizing who makes and executes the technology architecture. While

Introduction In a previous post, I presented feature extraction, which is a technique for utilizing pre-trained Large Language Models (LLMs) to solve a custom problem without having to retrain the model. Feature extraction is one of two ways to use the knowledge a model already has for a task that is different from what the model was originally trained to

Introduction In this post, I am going to present a technique that every engineer should know for utilizing open source large models. Specifically, I will show how to perform feature extraction. Feature extraction is one of two ways to use the knowledge a model already has for a task that is different from what the model was originally trained to

Tl;dr: In this post, we will create a custom image dataset and then train a You-Only-Look-Once (YOLO) model for the ubiquitous task of object detection. We will then implement a system using MinIO Bucket Notifications that can automatically perform inference on a new image. Introduction: Computer vision remains an extremely compelling application of artificial intelligence. Whether it’s recognizing

Introduction The California Gold Rush started in 1848 and lasted until 1855. It is estimated that approximately 300,000 people migrated to California from other parts of the United States and abroad. Economic estimates suggest that, on average, only half made a modest profit. The other half either lost money or broke even. Very few gold seekers made a significant