Explore software architectures used to model machine learning (ML) applications in production, as well as the building blocks of ML reference architecture, in this 11-video course. Examine the pitfalls and building approaches for evolutionary architectures, Fitness function categories, architectural planning guidelines for ML projects, and how to set up complete ML solutions. Learners will begin by studying the basic architecture required to execute ML in enterprises, and will also take a look at software architecture and its features that can be used to model ML apps in production. Next, learn how to set up model ML apps; examine ML reference architecture and the associated building blocks; and view the approaches for building evolvable architectures and migration. Recognize the critical pitfalls of evolutionary architecture and antipatterns of technical architecture and change. Finally, observe how to set up complete ML solutions and explore the Fitness function and its associated categories. Conclude the course with an exercise on architectural planning guidelines for ML projects, with a focus on model refinement, testing, and evaluating production readiness.