Distributed Machine Learning at Instacart
At Instacart, we take pride in offering a diverse range of machine learning (ML) products that empower every aspect of our marketplace, including customers, shoppers, retailers, and brands. Along with the growth of business and ML applications, we have encountered an increasing number of use cases that require distributed ML techniques to effectively scale our ML products.
Our team is currently working on a variety of ML applications that utilize the power of distributed computing to efficiently solve complex problems. Some examples include:
To meet these requirements, we have chosen Ray as the foundational computation framework for distributed ML. In this article, we will walk through the system architecture, analyze a case study, compare it with legacy systems, and demonstrate the value of Ray-based distributed ML.
We recognize that a system with a simple user experience is essential for increasing developer productivity and maximizing the impact of our distributed ML applications. Therefore, in addition to the design principles of scalability, resource efficiency, and diversity, we prioritize the following objectives to simplify the user experience:
