tutorials

EUMETNET E-AI is a programme "Artificial Intelligence and Machine Learning for Weather, Climate and Environmental Applications".

We are a community of weather services in Europe, with many partners from academia, research institutes and industry.

Collecting targeted tutorials for helping our scientists to learn about AI techniques and methods are being developed by many of our institutions. EUMETNET will work with tutorials and contribute to some of them, and make them accessible for our community.

Tutorial E-AI Basics 1: Intro, Environment, First Example

• 1.1 Basic Ideas of AI Techniques
• 1.2 Work Environment
• 1.3 First Example for AI - hands-on

Tutorial E-AI Basics 2: Dynamics, Downscaling, Data Assimilation Examples

• 2.1 Dynamic Prediction by a Graph NN
• 2.2 Data Recovery/Denoising via Encoder-Decoder
• 2.3 AI for Data Assimilation

Tutorial E-AI Basics 3: LLM Use, Transformer Example, RAG

• 3.1 Intro to LLM Use and APIs
• 3.2 Transformer for Language and Images
• 3.3 LLM Retrieval Augmented Generation (RAG)

Tutorial E-AI Basics 4: "MLOps" - Machine Learning Operations

• 4.1 Overview
• 4.2 MLOps in relation to traditional Weather forecasting
• 4.3 Road to MLOps

Tutorial E-AI Basics 5: MLflow - an open-source platform for managing the machine learning lifecycle

(in preparation)

• 5.1 Overview - User perspective
• 5.2 Logging to MLflow as a ML software developer
• 5.3 Running MLflow server as a user and as a service

Tutorial E-AI Basics 6: CI/CD - Continuous Integration and Continuous Deployment of ML codes

(in preparation)

• 6.1 Overview – What can CI/CD do for you?
• 6.2 Basic tests with Pytest
• 6.3 Setting up a runner