Academy AI

Academy/AI/1-Intro-To-ML-And-MLOps/.gitignore

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+# local mlflow
+mlartifacts/*
+mlruns/*
+dev/*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

Academy/AI/1-Intro-To-ML-And-MLOps/README.md

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+# Lab: Basic ML Model with Weather Dataset + MLflow Integration
+
+Welcome to this lab! Here you will learn how to:
+
+1. **Load and prepare a weather dataset**, with temperature and humidity data.
+2. **Train a Machine Learning model** using Scikit-learn, a powerful tool for Machine Learning in Python, to predict rain.
+3. **Evaluate the model** computing metrics to determine how well it makes predictions on new data.
+4. **Integrate MLflow**, one of the most used tool to track metrics, parameters, and model versions.
+
+We will follow a guided approach with detailed explanations at each step.  
+The first part focuses on Scikit-learn and the weather dataset. The second part extends the existing code with MLflow.
+
+---
+
+## Part 0: Setting Up the Python Environment
+
+Before we begin, we need to ensure we have everything required to run the lab properly.  
+
+### **Requirements**
+
+This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well.  
+In order to execute this laboratory, you will be asked to install a set of tools common in Machine Learning field:
+
+- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Machine learning library with modeling and evaluation tools.
+- [**pandas**](https://pandas.pydata.org/): Framework for data manipulation and analysis in Python.
+- [**seaborn**](https://seaborn.pydata.org/): Data visualization library based on Matplotlib.
+- [**mlflow**](https://mlflow.org/): Tool for tracking and managing machine learning experiments.  
+
+### 1. Clone the Repository
+
+To start, clone the lab repository by running the following command in the terminal:
+
+```sh
+  git clone https://github.com/kiratech/labs.git
+```
+
+### 2. Checkout the Lab Branch
+
+After cloning the repository, navigate to the project folder:
+
+```sh
+  cd labs
+```
+
+Then, checkout the `lab` branch:
+
+```sh
+  git checkout academy-ai
+```
+
+This folder contains resources related to AI-themed labs.
+
+### 3. Navigate to the 1-Intro-To-ML-And-MLOps Folder
+
+Go to the folder of the first lab:
+
+```sh
+  cd Academy/AI/1-Intro-To-ML-And-MLOps
+```
+
+### 4. Create a Virtual Environment
+
+A virtual environment allows you to isolate the project's dependencies from the system-wide ones.
+
+Using the **VSCode** terminal, create a virtual environment:
+
+```sh
+  conda create --name lab_env python=3.12 pip -y
+```
+
+Activate the Virtual Environment:
+
+```sh
+  conda activate lab_env
+```
+
+You should see the `(lab_env)` prefix in the terminal, indicating that the virtual environment is active.
+
+### 5. Install Required Packages
+
+Besides the default packages in the environment, additional libraries may be needed for the lab.  
+Before installing them, it's always a good practice to update `pip` to avoid compatibility issues:
+
+```sh
+  pip install --upgrade pip
+```  
+
+Now, install some essential packages for data analysis and machine learning:
+
+```sh
+  pip install scikit-learn pandas seaborn mlflow ipykernel
+```  
+
+Verify that the packages were installed correctly with:  
+
+```sh
+  conda list
+```  
+
+At this point, you can proceed with the `lab.ipynb` file.
+
+### 6. Open the Project in VSCode
+
+At this point, open VSCode from the file explorer or by running the command:
+
+```sh
+  code .
+```