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

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