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 :
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