Administrative information
| Title |
Data architecture |
 |
| Duration |
60 min |
| Module |
B |
| Lesson Type |
Tutorial |
| Focus |
Practical - Organisational AI |
| Topic |
Data architecture
|
Keywords
Data Architecture,Machine Learning pipeline,MLOps,
Learning Goals
- To know the fundamentals of Machine Learning System architectures
- To know how to design and automate a Machine Learning pipeline
- To know the basic aspects of some ML production pipeline
- To know how to configure a ML production pipeline on Cloud
Expected Preparation
Learning Events to be Completed Before
None.
Obligatory for Students
- Data Analysis Process
- Machine Learning Models
-
Optional for Students
References and background for students:
None.
Recommended for Teachers
- Google cloud architcture
- Bakshi, K. (2012, March). Considerations for big data: Architecture and approach. In 2012 IEEE aerospace conference (pp. 1-7). IEEE.
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
Topics to be covered
- Introduction to what a data architecture is
- MLOps
- Design and Automation of a Machine Learning pipeline
- Architecture of a Machine Learning system in production
- Orchestration of the ML pipeline.
- Configuration of a Continuous Integration
- Continuous Delivery CI/CD system for the ML pipeline using the Cloud.
Outline
| Duration (min) |
Description |
Concepts |
Activity |
Material |
| 5 |
MLOps |
ML System, ML Pipeline, DevOps,
Continuous Integration, Continuous Delivery, Continuous Testing
|
Lecture |
External Slides |
| 10 |
Design and Automation of a Machine Learning pipeline |
CI/CD Pipeline, CT Pipeline |
Lecture |
External Slides |
| 20 |
Architecture of a Machine Learning system in production |
Data validation, Preprocessing,
Model Development, Model Analysis, Model Deployment, TFX
|
Lecture |
External Slides |
| 10 |
Orchestration of the ML pipeline |
Orchestrator, Kubeflow |
Lecture |
External Slides |
| 15 |
Configuration of a Continuous Integration/ Continuous Delivery CI/CD system for the ML pipeline using the Cloud |
ML pipeline on Cloud |
Running Example |
Online Tutorials |
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
|
