Interactive session: Data architecture

Interactive session: Data architecture

Administrative information

Title Data architecture
Duration 60 min
Module B
Lesson Type Interactive Session
Focus Practical - Organisational AI
Topic

Data architecture

 

Keywords

Data Architecture,Machine Learning pipeline​,MLOps​,

 

Learning Goals

  • To know the basic data architectures in Machine Learning
  • Pose questions about the most suited data architectures

 

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

  • Data Analysis Process
  • Machine Learning Models
  • DevOps
  • CI/CD

Optional for Students

None.

References and background for students:

  • DevOps
  • CI/CD

Recommended for Teachers

Lesson Materials


The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers

Use the following outline:

  • Introduction to the discussion
    • What are the most diffused architectures for ML Systems?
    • What is a typical ML pipeline?
    • What is the MLOps?
    • How it is possible to automate and orchestrate a ML pipeline?
    • How it is possible configure a Continuous Integration/ Continuous Delivery CI/CD system for the ML pipeline using the Cloud?
    • Questions and further discussion on topics suggested by students
  • Discussion
    • What are the characteristics of the Tensor Flow eXTended (TFX) architecture?
    • How can Cloud support the TFX model?
    • How How it is possible to automate and orchestrate the TFX pipeline?
    • How it is possible configure a Continuous Integration/ Continuous Delivery CI/CD system for the TFX pipeline?
    • Questions and further discussion on topics suggested by students
  • Conclusions
    • Summing up and discussing the lesson outcomes:
      • Main features of an ML System Architecture and of ML pipelines
      • MLOPs
      • Automating and orchestrating a ML pipeline with reference to the TFX model
    • Conclusive remarks

Time schedule

 
Duration (min) Description Concepts
20 Introduction to the discussion ML System Architecture, ML Pipeline
30 Discussion ML in production examples
10 Summing up and conclusive remarks  

 

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium

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Het arrangement Interactive session: Data architecture is gemaakt met Wikiwijs van Kennisnet. Wikiwijs is hét onderwijsplatform waar je leermiddelen zoekt, maakt en deelt.

Auteur
HCAIM Consortium
Laatst gewijzigd
2024-05-15 11:11:15
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Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

https://maken.wikiwijs.nl/198386/Acknowledgement

HCAIM Consortium. (z.d.).

Practical: ML-Ops Lifecycle

https://maken.wikiwijs.nl/200288/Practical__ML_Ops_Lifecycle

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