Administrative information

Title MLOPs Life Cycle
Duration 60 min
Module B
Lesson Type Practical
Focus Practical - Organisational AI
Topic

End-to-end overview of the MLOPs lifecycle

 

Keywords

MLOPs,Organizational AI,Ethical,Design,

 

Learning Goals

 

Expected Preparation

Learning Events to be Completed Before

Optional for Students

  • Data Preparation and Management: Before diving into MLOps, it's beneficial to understand the initial phases of the machine learning process, especially data collection, cleaning, and preprocessing
  • Model Training and Validation: A grasp of how models are trained, validated, and evaluated will provide a solid foundation for understanding the operational aspects of ML.
  • Hyperparameter Tuning: While not always covered in depth in MLOps courses, understanding hyperparameter tuning can be beneficial as it's a crucial step in model optimization.
  • MLOps Tools and Platforms: Familiarity with tools like Kubeflow, Azure ML, and others can give students a head start.
  • Documentation Practices in ML: Proper documentation is essential in MLOps for reproducibility and collaboration. Understanding best practices in ML documentation can be advantageous.
  • CRISP-DM, CRISP-ML, ML Canvas: These are methodologies and frameworks for ML project management. Having a basic understanding can be beneficial for the operational side of ML projects.

References and background for students:

Recommended for Teachers

  • JupyterNotebooks
  • CUE
  • Docker
  • Papermill
  • Streamlit
  • Shell Scripting

 

Lesson Materials

 

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

 

Instructions for Teachers

Introduction

Before teaching the practical MLOps course, it's essential for teachers to have a deep understanding of the tools and technologies mentioned in the course outline. The course is structured around a 3-part demo, each highlighting a different approach to MLOps. Here are the steps to familiarize yourself with these tools:

1. The Shell (Manual) Way

Objective: Understand the basics of setting up a Python environment and running a Jupyter notebook.

Steps:

Python Environment: Install Python on your system. Learn how to create a virtual environment using venv or conda. Practice activating and deactivating the environment.

Package Installation: Understand the structure and purpose of a requirements.txt file. Practice installing packages using pip install -r requirements.txt.

Jupyter Notebook: Install Jupyter Notebook. Learn the basics of starting a Jupyter server. Practice creating, running, and saving notebooks.

Streamlit Server: Understand the purpose of Streamlit and how it can be used to create web applications. Practice using artifacts generated from a Jupyter notebook in a Streamlit application.

Considerations: Understand the benefits of this approach, such as building foundational knowledge and initial prototyping. Be aware of its limitations, like the potential messiness, challenges in knowledge transfer, and difficulties in replicating setups.

2. The Docker + Make (Inheritance) Way

Objective: Grasp the concepts of containerization and automation using Docker and Make.

Steps:

Docker: Install Docker on your system. Understand the structure and purpose of a Dockerfile. Practice building Docker images and running containers. Familiarize yourself with common Docker commands.

Makefile:Understand the purpose of a Makefile in automating tasks. Learn the basic syntax of a Makefile. Practice writing and executing simple Make commands.

Considerations: Understand the benefits of this approach, such as reproducibility. Recognize its limitations, like the need for replication across projects, context switching, and its focus on images over artifacts.

3. The Radix (Compositional) Way

Objective: Dive into advanced MLOps practices using compositional workflows.

Steps:

Papermill: Understand the purpose of Papermill in parameterizing and executing Jupyter notebooks. Practice creating and running notebooks with Papermill. Explore available packages or consider creating a simple one.

Streamlit (Advanced): Dive deeper into advanced Streamlit functionalities. Explore available packages or consider creating a simple one.

Workflow Creation: Understand the concept of compositional workflows in MLOps. Practice creating workflows that utilize the Papermill and Streamlit packages.

Considerations: Understand the benefits of this approach, such as reproducibility, easy parameterization, validation, and composability. Be aware of its limitations, like being on the bleeding edge and potential gaps in documentation.

Conclusion

By following these steps, teachers will be well-equipped to deliver the practical MLOps course effectively. It's crucial to not only understand the technical aspects but also the underlying reasons for choosing each approach. This will enable teachers to provide students with a comprehensive understanding of MLOps practices.

Outline

Outline/time schedule
Duration (Min) Description
30 The Manual Way - Shell Scripting
30 The Docker + Make (Inheritance) way
30 The Radix (Compositional) way
30 Runtime, Tooling and Performance considerations
10 Summarize and evaluate results

 

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