Building large scale machine learning(ML) infrastructures remains a challenge for most companies. While we are all dazzled by the advent of foundation models, the reality is that the complexity of the requirements to build enterprise solutions with those models have skyrocketed. As a result, it is always a good practice to learn from teams that are at the forefront of ML architectures. Companies like Uber, LinkedIn, Walmart, Shopify, Yelp have spent considerable amounts of time and resources building their internal ML platforms and many of those lessons can be applied to other architectures. Pinterest is one of those companies rapidly innovating in their ML infrastructure. Recently, they built a new iteration of their internal ML platform known as MLEnv. Today, I would like to share some details about the architecture and capabilities of the platform powering ML workloads at Pinterest.

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