Updated the text to match the figure from Andrea

workflow.qmd

@@ -27,15 +27,16 @@ The ML workflow is a structured approach that guides professionals and researche
 
 ![Multi-step design methodology for the development of a machine learning model. Commonly referred to as the machine learning lifecycle](./images/ML_life_cycle.png)
 
-An ML workflow is a systematic process that encompasses the development, deployment, and maintenance of ML models. The typical steps involved are:
-
-1. **Problem Definition**: Clearly define the problem you aim to solve with your ML model, whether it's image classification, customer churn prediction, or text generation. This clarity sets the stage for data collection and algorithm selection.
-2. **Data Collection and Preparation**: Gather a high-quality dataset that accurately represents the problem at hand. Data cleaning and preparation are essential steps, which may include outlier removal, missing value imputation, and feature scaling.
-3. **Algorithm Selection**: Choose an ML algorithm that aligns with your data type and problem. Various algorithms have their own pros and cons, making the selection critical.
-4. **Model Training**: Train your chosen ML algorithm on the prepared dataset. The duration of this process can vary based on dataset size and complexity.
-5. **Model Evaluation**: Assess the model's performance using a separate test set to gauge its generalization capabilities.
-6. **Model Deployment**: Integrate the model into production once its performance meets your criteria. This could involve embedding it into a software application or offering it as a web service.
-7. **Monitoring and Maintenance**: Keep track of the model's performance post-deployment and update it as necessary to adapt to changing real-world conditions.
+Developing a successful machine learning model requires a systematic workflow. This end-to-end process enables you to build, deploy and maintain models effectively. It typically involves the following key steps:
+
+1. **Problem Definition** - Start by clearly articulating the specific problem you want to solve. This focuses your efforts during data collection and model building.
+2. **Data Collection to Preparation** - Gather relevant, high-quality training data that captures all aspects of the problem. Clean and preprocess the data to get it ready for modeling. 
+3. **Model Selection and Training** - Choose a machine learning algorithm suited to your problem type and data. Consider pros and cons of different approaches. Feed the prepared data into the model to train it. Training time varies based on data size and model complexity. 
+4. **Model Evaluation** - Test the trained model on new unseen data to measure its predictive accuracy. Identify any limitations.
+6. **Model Deployment** - Integrate the validated model into applications or systems to start operationalization. 
+7. **Monitor and Maintain** - Track model performance in production. Retrain periodically on new data to keep it current.
+
+Following this structured **ML workflow** helps guide you through the key phases of development. It ensures you build effective and robust models that are ready for real-world deployment. The end result is higher quality models that solve your business needs.
 
 The ML workflow is iterative, requiring ongoing monitoring and potential adjustments. Additional considerations include: