In this session, Stephen Balaban, CEO of Lambda, shares a playbook for standing up machine learning infrastructure. This session is intended for any organization that has to scale up its infrastructure to support growing teams of Machine Learning (ML) practitioners. Stephen describes how large ML models are often built with on-premise infrastructure. He explores the pros and cons of this approach and how the workstations, servers, and other related resources could be scaled up to support larger workloads or numbers of users. How do you scale from a single workstation to a number of shared or dedicated servers for each ML practitioner? How can you use a single software stack across laptops, servers, clusters, and the cloud? What are the network, storage, and power considerations of each step in that journey? Join this session to hear some best practices for scaling up your machine learning platform, to serve the growing needs of your organization.