AI/ML

A chain is as strong as its weakest link - and your AI/ML infrastructure is only as fast as your slowest component. If you train machine learning models with GPUs, then your weak link may be your storage solution. The result is what I call the “Starving GPU Problem.”  The Starving GPU problem occurs when your network or your

Explore the essential role of Data Engineers in unleashing the true power of AI! Data Engineers have a critical foundation in cleaning and structuring raw data for ML success. Learn why their expertise in data infrastructure, feature engineering, and pipeline optimization is indispensable.

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.

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