fix merge

Author: fmind

README.md

@@ -38,13 +38,13 @@ Whether you are a beginner eager to explore or an experienced professional seeki
 To start contributing , you will need to set up your development environment:
 
 1. Clone the repository.
-2. Install dependencies using [uv](https://docs.astral.sh/uv/):
+2. In the cloned repostory directory, install dependencies using [uv](https://docs.astral.sh/uv/):
 
 ```bash
 invoke install
 ```
 
-3. Serve the documentation locally to see course material in your browser:
+3. Serve the documentation locally (from that directory) to see course material in your browser:
 
 ```bash
 invoke serve

docs/1. Initializing/1.1. Python.md

@@ -42,5 +42,5 @@ For new projects, [the latest Python version](https://www.python.org/downloads/)
 - [Real Python Tutorials](https://realpython.com/)
 - [Learn Python in Y minutes](https://learnxinyminutes.com/docs/python/)
 - [Best of Python](https://github.com/ml-tooling/best-of-python)
-- [Best oof Python ML](https://github.com/ml-tooling/best-of-ml-python)
+- [Best of Python ML](https://github.com/ml-tooling/best-of-ml-python)
 - [Awesome Python](https://github.com/vinta/awesome-python)

docs/2. Prototyping/2.6. Evaluations.md

@@ -168,5 +168,5 @@ for param_name, param_range in PARAM_GRID.items():
 ## Evaluations 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)).
+- [Data Leakage in Machine Learning](https://en.wikipedia.org/wiki/Leakage_(machine_learning))
 - [Model selection and evaluation](https://scikit-learn.org/stable/model_selection.html)

docs/5. Refining/5.5. AI-ML Experiments.md

@@ -167,4 +167,4 @@ mlflow.log_metric(key="train_loss", value=train_loss, step=epoch, timestamp=now)
 - **[AI-ML Experiment integration from the MLOps Python Package](https://github.com/fmind/mlops-python-package/blob/main/src/bikes/io/services.py)**
 - [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html)
 - [Experiment Tracking with MLflow in 10 Minutes](https://towardsdatascience.com/experiment-tracking-with-mlflow-in-10-minutes-f7c2128b8f2c)
-- [How We Track Machine Learning Experiments with MLFlow](https://www.datarevenue.com/en-blog/how-we-track-machine-learning-experiments-with-mlflow)
+- [How We Track Machine Learning Experiments with MLFlow](https://medium.com/towards-data-science/how-we-track-machine-learning-experiments-with-mlflow-948ff158a09a)

docs/7. Observability/index.md

@@ -13,4 +13,3 @@ Observability in Machine Learning Operations (MLOps) is crucial for gaining insi
 - **[7.4. Costs and KPIs](./4. Costs-KPIs.md)**: Explore techniques for managing costs associated with running AI/ML workloads and for defining and tracking key performance indicators (KPIs) aligned with business goals, using MLflow Tracking for analysis.
 - **[7.5. Explainability](./5. Explainability.md)**: Explore the concept of explainable AI, focusing on techniques like SHAP to understand model predictions and build trust in AI systems.
 - **[7.6. Infrastructure](./6. Infrastructure.md)**: Discover the importance of infrastructure monitoring, learning how to track resource usage and performance metrics to optimize efficiency and costs through MLflow System Metrics.
-```