Machine Learning in Cloud Infrastructures

Transform a trained model into web service with UI using Streamlit.

Under development: This learning path is in an early stage of development and may change significantly.

Module 1: Introduction to basic Unix commands

Learn the basic Unix shell commands used in cloud environments.

Tutorial Slides Description Learning Objectives Estimated Time
Introduction to basic Unix commands This is the introduction to the basic shell commands.
  • Describe key reasons for learning shell.
  • Navigate your file system using the command line.
  • Access and read help files for `bash` programs and use help files to identify useful command options.
  • Demonstrate the use of tab completion, and explain its advantages.
2H

Module 2: Cloud Concepts

Understand cloud architecture, providers, and services.

Tutorial Slides Description Learning Objectives Estimated Time
SimpleVM Workshop Tutorial shows how to identify pathogenic bacteria within publicly available metagenomic datasets and scale up a bioinformatics workflow using SimpleVM.
  • Learn how to configure a virtual machine and scale it up with more resources.
2H

Module 3: ML model deployment

Understand cloud architecture, providers, and services.

Tutorial Slides Description Learning Objectives Estimated Time
Setting up ML project in the cloud infrastructure Transform a trained model into an online service with a UI using Streamlit.
  • Learn how to create, configure, and access a VM instance in the de.NBI Cloud using SimpleVM.
  • Learn basics of Streamlit syntax and deploy a trained ML model as an interactive web application.
5H
Deploying Machine Learning workflow using FastAPI
  • Learn best practices to serve model with FastAPI and get predictions as a table.
3H

Module 4: Cloud based data handling

Handle large datasets using cloud object storage

Tutorial Slides Description Learning Objectives Estimated Time

Module 5: ML monitoring, retraining, and automation

Tutorial Slides Description Learning Objectives Estimated Time
Contributions

Author(s): Dilfuza Djamalova

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👥 Contribution: We also welcome contributions when you spot an opportunity to improve the training materials. Please review the contribution page first. Then, edit this material on GitHub to suggest your improvements.