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.
Enterprise Architecture: Design Architecture for Machine Learning Applications
discover the key concepts covered in this course
describe the basic architecture required to execute machine learning implementations in the enterprise
describe software architectures and their associated features that can be used to model machine learning applications in production
set up an enterprise architecture to implement a robust memory model
describe machine learning reference architecture and the associated building blocks of the reference architecture
describe approaches for building evolvable architectures and migrating architectures
recognize the pitfalls of evolutionary architecture and the antipatterns of technical architecture and incremental change
demonstrate how to set up complete machine learning solutions
describe the Fitness function and its associated categories
recognize architectural planning guidelines for machine learning projects, with focus on model refinement, testing, and evaluating production readiness