AI, Architecture and Machine Learning

Remember agile architects’ tightrope walk between flexibility/performance and testability/reliability/predictability/V&V? Machine Learning (ML) is leveling up the same tradeoff. 
Whether you’re just starting to keep an eye on ML, or are already adjusting your architecture to the current wave of AI, this course addresses several issues most architects will be facing in an architectural landscape where ML is a fast-grower. The course is not tied to a particular vendor, language, framework, library, or environment.

Introduction: Abstraction levels, AI, ML’s past and present

Abstraction levels: architect versus devs
AI apps overview: pushing up the bar for intelligent systems
The evolution of ML apps:  from slow and auditable, to fast smart and opaque. Despite ML’s roots (rule induction and mining) in the successful decryption of unbreakable ciphers, its near future seems encoded as weight values in deep neural networks. Whereas for example black-box flight recorders clarified the chain of events & decisions in past emergencies, ML has now, paradoxically, landed in black boxes that hide opaque logic.
A short exercise 
Predictions for the next 10-18 months: AI investment, and tech challenges to cope with.
A short exercise

Consequences in architecture, analytics, HMI, RT

NFR and Quality Attributes, businesses where “better on average” is no longer “good enough” as it was in recommender engines or marketing to layman consumers
Three categories of ML
Deep Learning
The comeback of Data Architecture in Software Architecture
The increasing overlap  RT – DB apps, and edge ML & computing
The two-way street: Architecture for AI, and AI for Architecture.
A short exercise

The role of architects 

What is, and is not, in the job description of an architect 
Perspective (broad, integrated) compared to others (dev, researcher, prime mover) 
The systems portfolio and digital twins (“avatars”) cross-connecting perspectives
Architectural tactics, thinking, abstraction level, and background
A short exercise 
Agile architecture, variability, and edge ML. 

Tackling the leveled-up tradeoff

Top-down: a framework of corporate values and procedures to boost V&V and transparency 
Bottom-up: tech and practices in IT to build in transparency mechanisms upfront (visualization, explanation, traceability, etc.)
Explainable AI (XAI) “Big bang,” via research into self-explanation capabilities for deep neural networks 
Corporate “think big, start small,” XAI via hybrid AI.