Enterprise Architecture: Design Architecture for Machine Learning Applications

Machine Learning
  • 11 Videos | 1h 4m 4s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    57s
    UP NEXT
  • Playable
    2. 
    Architecture for ML in Enterprises
    5m 58s
  • Locked
    3. 
    Software Architecture to Model ML Apps in Production
    14m 7s
  • Locked
    4. 
    Model Machine Learning Apps
    5m 8s
  • Locked
    5. 
    ML Reference Architecture and Building Blocks
    5m 25s
  • Locked
    6. 
    Evolvable Architectures and Migration
    9m 37s
  • Locked
    7. 
    Pitfalls of Evolutionary Architecture and Antipatterns
    5m 14s
  • Locked
    8. 
    Setting Up ML Solutions
    4m 42s
  • Locked
    9. 
    Fitness Function and Categories
    2m 51s
  • Locked
    10. 
    Architecture for Refinement and Production Readiness
    4m 24s
  • Locked
    11. 
    Course Summary
    1m 11s

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