The machine learning infrastructure space is complex and crowded, with various platforms offering different functions across the model building workflow. Understanding the goals and challenges of each stage of the workflow can help businesses make informed decisions on which ML infrastructure platforms to use. The production environment is a critical part of the model lifecycle, where the model touches the business and makes decisions that improve outcomes or cause issues for customers. However, transitioning from a research environment to a production engineering environment poses unique challenges, such as moving from rapid experimentation in Jupyter Notebooks to software engineering environments with version control, test coverage analysis, and reproducibility. Model validation is critical to delivering models that work in production, involving testing model assumptions, demonstrating how well a model will work under different environments, and ensuring the model's performance matches expectations. ML infrastructure tools can help with model validation by providing repeatable and reproducible tests, enabling organizations to reduce time to operationalize models and deliver models with confidence.