April 2024 Summaries

Cron jobs are a way to automate work by running scripts at regular intervals, such as every hour or daily. They use a specific syntax to specify the schedule, which includes five numbers representing minute, hour, day of month, month, and day of week. Cron uses the host machine's time zone, typically UTC, and can be used for various tasks like data jobs, sending reports or alerts, and system maintenance. However, cron has limitations, including being primarily made for shell scripts, lacking monitoring, and requiring cloud provisioning. Modal is an alternative to cron jobs that allows scheduling Python code directly with basic monitoring capabilities.

With just a handful of examples, fine-tuned open-source embedding models can provide greater accuracy at a lower price than proprietary models. Custom models matter for companies like Netflix and Spotify that use data to improve their recommendation systems. Large pre-trained models with permissive licenses have simplified the bootstrap step, allowing organizations to start with these models and expect them to perform reasonably well on their task. Fine-tuning kicks off the data flywheel by accumulating data quickly, which can lead to better performance than the off-the-shelf model. The process of fine-tuning involves design decisions such as finding or creating a dataset, choosing a base model, and acquiring training infrastructure. Running a grid search over fine-tuning hyperparameters is an effective way to explore experimental parameters, and Modal's autoscaling infrastructure can be used to scale experiments in parallel. Even with just a few hundred examples, it's possible to beat proprietary models on a simple question-answering task, and moving forward, the next step would be to operationalize this process to collect more data and iterate on the model.