Much has been said lately about the wonders of Large Language Models (LLMs). Most of these accolades are deserved. Ask ChatGPT to describe the General Theory of Relativity and you will get a very good (and accurate) answer. However, at the end of the day ChatGPT is still a computer program (as are all other LLMs) that is blindly executing

Over the past few months, I have written about a number of different technologies (Ray Data, Ray Train, and MLflow). I thought it would make sense to pull them all together and deliver an easy-to-understand recipe for distributed data preprocessing and distributed training using a production-ready MLOPs tool for tracking and model serving. This post integrates the code I presented

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.

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

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

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

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

Introduction In a few previous posts on AI/ML, I mentioned that one of the benefits of MinIO is that you have tools for Versioning, Lifecycle Management, Object Locking, Object Retention and Legal Holds. These capabilities have a variety of uses. You may need a simple way to keep track of training experiments. You could also use these features to

This post first appeared in The New Stack. Developers gravitate to technologies that are software defined, open source, cloud native and simple. That essentially defines object storage. Introduction Choosing the best storage for all phases of a machine learning (ML) project is critical. Research engineers need to create multiple versions of datasets and experiment with different model architectures. When a