Making the serving of your AI models more lightweight by leveraging the simplicity of MinIO’s object store. tl;dr MinIO object storage can be used as a ‘single source of truth’ for your machine learning models and, in turn, make serving with PyTorch Serve more efficient when managing changes to Large Language Models (LLMs). As always, sample code is

This post was written in collaboration with Harinder Mashiana from cnvrg.io. Large language models (LLMs) have revolutionized the world of technology, offering powerful capabilities for text analysis, language translation, and chatbot interactions. The revolution will heavily impact businesses, according to OpenAI, approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by

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