The source code for the experiment mentioned in this article is available on GitHub.

I was studying chapter 9 of D2L recently on natural language processing (NLP) using recurrent neural networks (RNN). To validate and further reinforce my understanding of the concepts presented in the chapter, I decided to train a word-level language model from scratch based on the King James Version (KJV) of the Christian Bible.

The notebook source code for this experiment is available on GitHub.

“MLflow is the largest open source AI engineering platform for agents, LLMs, and ML models.” It was originally developed by Databricks and joined the Linux Foundation in June 2020.

MLflow enables data scientists and ML engineers to manage and visualize their model training pipelines through a unified platform. At the heart of MLflow are 2 key concepts.

1. Experiment: a collection of model training pipelines useful for cross-evaluating the performance of different models and hyperparameters on the the same problem domain. For example, an experiment called fashion-mnist-linear compares the performance of training the same linear classifier with varying learning rates.
2. Run: a single instance of a model training pipeline within an experiment. For example, our first run in fashion-mnist-linear trains a linear classifier with a learning rate of 0.01.