Machine learning models can be simplified using relational data predictions with algorithms like linear regression and XGBoost.
Implementing PostgresML in databases can solve real-time join problems, streamline data processing, and simplify model training.
Deep dives
Simplifying Machine Learning Workflows
In practical AI applications at Instacart, complex machine learning models can often be simplified to tackling relational data predictions. Utilizing linear regression, XGBoost, and other algorithms on relational data with select functionalities like joins can yield accurate predictions without leaning heavily on deep learning methods.
The Evolution of Machine Learning Solutions at Instacart
At Instacart, transitioning from monolithic architectures to more distributed platforms presented challenges in scaling machine learning models. Implementing Postgres ML involved solving join problems in real-time to streamline data processing and reduce system complexities encountered during rapid growth.
The Role of Feature Engineering in Machine Learning
Feature engineering and data cleaning play crucial roles in practical AI applications. Postgres ML offers tools to train models efficiently by allowing users to choose algorithms, compare results, and simplify the process of transforming data for varied prediction types such as classification and regression.
Embracing Simplicity and Ergonomics in Machine Learning Workflow
Anticipated future enhancements in Postgres ML focus on promoting simplicity and user-friendly interfaces for deploying models. The aim is to streamline operations, automate processes, and reduce the technical complexities involved in handling machine learning solutions, fostering a more enjoyable and efficient experience for data scientists.
While scaling up machine learning at Instacart, Montana Low and Lev Kokotov discovered just how much you can do with the Postgres database. They are building on that work with PostgresML, an extension to the database that lets you train and deploy models to make online predictions using only SQL. This is super practical discussion that you don’t want to miss!