Update Chapter 7

Author: fmind

README.md

@@ -16,7 +16,7 @@ Whether you are a beginner eager to explore or an experienced professional seeki
 - **Hands-on Python Coding**: Learn to code with Python in a way that's directly applicable to real-world AI projects.
 - **Project-Driven Learning**: Each chapter includes practical project instructions to help you apply what you've learned.
 - **MLOps Techniques**: Gain insights into effective MLOps coding strategies that streamline the development and deployment of AI/ML models.
-- **Open Source Tools**: Familiarize yourself with industry-standard tools like [uv](https://docs.astral.sh/uv/), [pytest](https://docs.pytest.org/en/latest/), [pyenv](https://github.com/pyenv/pyenv), [ruff](https://docs.astral.sh/ruff/), [mlflow](https://mlflow.org/), [bandit](https://bandit.readthedocs.io/en/latest/), [pre-commit](https://pre-commit.com/), [GitHub](https://github.com/), and [VS Code](https://code.visualstudio.com/).
+- **Open Source Tools**: Familiarize yourself with industry-standard tools like [uv](https://docs.astral.sh/uv/), [pytest](https://docs.pytest.org/en/latest/),/mlflow.org/), [bandit](https://bandit.readthedocs.io/en/latest/), [pre-commit](https://pre-commit.com/), [GitHub](https://github.com/), and [VS Code](https://code.visualstudio.com/).
 - **Mentoring sessions**: Boost your learning experience with personalized feedback and expert insights from the course authors.
   - Book [a one-on-one mentoring session](https://calendar.app.google/9KfEBkpCHQKwarLF8) to receive tailored guidance and support on the course.
   - Contact the [course creators](mailto:[email protected]) to request a personalized quote for group and organization training.

docs/1. Initializing/1.1. Python.md

@@ -48,7 +48,7 @@ This is a foundational practice in modern software development and is non-negoti
 
 ## How should I install Python for this course?
 
-We recommend using [`uv`](https://docs.astral.sh/uv/guides/install-python/) to manage Python installations. `pyenv` lets you easily install and switch between different Python versions on a per-project basis, preventing conflicts with your system's default Python. This flexibility is invaluable in development.
+We recommend using [`uv`](https://docs.astral.sh/uv/guides/install-python/) to manage Python installations. `uv` lets you easily install and switch between different Python versions on a per-project basis, preventing conflicts with your system's default Python. This flexibility is invaluable in development.
 
 For production environments, especially those in containers, you have the freedom to specify the exact Python version you need, ensuring perfect alignment between your development and production setups.
 

docs/2. Prototyping/2.0. Notebooks.md

@@ -55,7 +55,7 @@ Saving notebook outputs in your Git repository is generally not recommended due
 
 Instead of saving outputs directly in the repository, consider using tools like [Jupyter's nbconvert](https://nbconvert.readthedocs.io/en/latest/) to export notebooks to different formats (e.g., HTML, PDF) that can be shared on other platforms or included in documentation. You can also use [Jupyter's built-in cell tags](https://jupyterbook.org/interactive/hiding.html) to hide or exclude specific cells from the exported version, allowing you to control what information is shared while keeping the repository clean and focused on code.
 
-## Notebook additional resources
+## Additional Resources
 
 - **[Notebook example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [Jupyter Website](https://jupyter.org/)

docs/2. Prototyping/2.1. Imports.md

@@ -108,7 +108,7 @@ print("Module search paths:", sys.path)
 
 Adjusting these paths or ensuring the correct virtual environment is activated can often resolve issues related to package imports. With VS Code, you can [select the Python environment](https://code.visualstudio.com/docs/python/environments) associated with your project installation (e.g., `.venv`).
 
-## Imports additional resources
+## Additional Resources
 
 - **[Imports example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [Python import: Advanced Techniques and Tips](https://realpython.com/python-import/)

docs/2. Prototyping/2.2. Configs.md

@@ -146,6 +146,6 @@ import sklearn
 sklearn.set_config(transform_output='pandas')
 ```
 
-## Configs additional resources
+## Additional Resources
 
 - **[Configs example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**

docs/2. Prototyping/2.3. Datasets.md

@@ -117,7 +117,7 @@ Whether to shuffle your dataset before splitting it into training and testing se
 
 For datasets where time or sequence does not impart any context to the data, shuffling helps to ensure that the training and testing sets are representative of the overall dataset, preventing any unintentional bias that might arise from the original ordering of the data. This is especially important in scenarios where the dataset may have been sorted or is not randomly distributed (e.g., sorted by price).
 
-## Datasets additional resources
+## Additional Resources
 
 - **[Dataset example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [10 minutes to pandas](https://pandas.pydata.org/docs/user_guide/10min.html)

docs/2. Prototyping/2.4. Analysis.md

@@ -93,7 +93,7 @@ msno.bar(data)     # Bar chart of non-missing values
 
 These visualizations help identify patterns and distributions of missing data, aiding in effective preprocessing decisions.
 
-## Analysis additional resources
+## Additional Resources
 
 - **[Example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [10 minutes to pandas](https://pandas.pydata.org/docs/user_guide/10min.html)

docs/2. Prototyping/2.5. Modeling.md

@@ -185,7 +185,7 @@ In this way, the final model embodies the culmination of your exploratory work,
 
 It's important to note, however, that while retraining on the full dataset can improve performance, it also eliminates the possibility of evaluating the model on unseen data unless additional, separate validation data is available. Therefore, the decision to retrain should be made with consideration of how model performance will be assessed and validated post-retraining.
 
-## Modeling additional resources
+## Additional Resources
 
 - **[Example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [Supervised learning](https://scikit-learn.org/stable/supervised_learning.html)

docs/2. Prototyping/2.6. Evaluations.md

@@ -165,7 +165,7 @@ for param_name, param_range in PARAM_GRID.items():
 
 ![Evaluation validation curve](../img/evaluations/validation_curve.png)
 
-## Evaluations additional resources
+## Additional Resources
 
 - **[Example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/notebooks/prototype.ipynb)**
 - [Data Leakage in Machine Learning](https://en.wikipedia.org/wiki/Leakage_(machine_learning))

docs/4. Validating/4.3. Logging.md

@@ -163,7 +163,7 @@ For MLOps, this means you can create powerful dashboards and alerts that monitor
 7.  **Implement Log Rotation**: To prevent log files from consuming excessive disk space, configure log rotation to automatically archive or delete old files based on size or age. `loguru` handles this automatically when logging to a file.
 8.  **Correlate Logs with Traces**: In complex systems, use a correlation ID (or trace ID) to link all log messages generated from a single request or transaction as it flows through different services.
 
-## Logging additional resources
+## Additional Resources
 
 - **[Logging example from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/src/bikes/jobs/training.py)**
 - [A Complete Guide to Logging in Python with Loguru](https://betterstack.com/community/guides/logging/loguru/)