T1913

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AI, Architecture, and Machine Learning

Remember the architect’s tightrope walk between flexibility/performance and testability/reliability/predictability/V&V? Right now, 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.

Audience

- Architects, modelers, informaticians, analysts, designers, tech managers, domain experts, and other roles involved in/affected by AI projects.

Prior knowledge

Agile Architecture Fundamentals (course T1101) or corresponding experience/knowledge of IT architecture in an agile setup. Even experience from other roles in the IT field is useful, not least from knowledge-intensive sectors or AI-related.

Courseware

Presentation slides and short exercise, in English.

Course Outline: 

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 step-by-step landed in black boxes that hide opaque logic.
  • Predictions for the next 10-12 months: AI investment, and tech challenges to cope with.

Consequences in architecture, analytics, HMI, RT

  • NFR, QA, moving toward businesses where “better on average” is not “good enough” as it was in marketing or recommender engines for billions of layman consumers
  • Three categories of ML
  • Deep Learning
  • The comeback of Data Architecture in Software Architecture
  • The increasing overlap  RT – DB apps, and edge-device ML & computing
  • The two-way street: Architecture for AI, and AI for Architecture.

A brief exercise

The role of architects

  • What is, and is not, in the job description of an architect
  • Perspective (broad, integrated) compared to others (dev,... Läs mer

Course Outline: 

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 step-by-step landed in black boxes that hide opaque logic.
  • Predictions for the next 10-12 months: AI investment, and tech challenges to cope with.

Consequences in architecture, analytics, HMI, RT

  • NFR, QA, moving toward businesses where “better on average” is not “good enough” as it was in marketing or recommender engines for billions of layman consumers
  • Three categories of ML
  • Deep Learning
  • The comeback of Data Architecture in Software Architecture
  • The increasing overlap  RT – DB apps, and edge-device ML & computing
  • The two-way street: Architecture for AI, and AI for Architecture.

A brief 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
  • 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.

End of the course  

About Teacher

Milan Kratochvil, teacher Informator UtbildningMilan Kratochvil , senior consultant, Kiseldalen.com (Sweden). Main author: UML Extra Light (Cambridge University Press) and Growing Modular (Springer).

Since the mid 1990-ies, he also cooperates with Informator-Tieturi, Scandinavia’s largest training provider for IT-professionals.

Utbildningen levereras i samarbete med

Kursfakta

Kurs-ID: T1913
Längd: 1 dag
Pris exkl moms: 9 900 kr
Inregistrering: 09.00
Kursstart: 09.30
Kursslut (ca): 17.00

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Stockholm
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13 dec
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Göteborg
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13 dec
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