add exercise 2

episodes/5-releasing-a-model.md

@@ -52,3 +52,35 @@ Data scarcity
 * **Resource efficiency**: Instead of training a model from the ground up, practitioners can use existing models as a starting point, saving time, computational resources, and energy.
 
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+:::::::::::::::::::::::::::::::::::::: challenge
+
+### What pieces must be well-documented to ensure reproducible and responsible model sharing?
+Discuss in small groups and report out: *Why do you believe it is or isn’t important to share ML models? How has model-sharing contributed to your experiences or projects?*
+
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+:::::::::::::: solution
+
+### Solution
+
+* Environment setup
+* Training data
+  * How the data was collected
+  * Who owns the data: data license and usage terms
+  * Basic descriptive statistics: number of samples, features, classes, etc.
+  * Note any class imbalance or general bias issues
+  * Description of data distribution to help prevent out-of-distribution failures.
+* Preprocessing steps. 
+  * Data splitting
+  * Standardization method
+  * Feature selection
+  * Outlier detection and other filters
+* Model architecture, hyperparameters and, training procedure (e.g., dropout or early stopping)
+* Model weights
+* Evaluation metrics. Results and performance. The more tasks/datasets you can evaluate on, the better.
+* Ethical considerations:  Include investigations of bias/fairness when applicable (i.e., if your model involves human data or affects decision-making involving humans)
+* Contact info
+* Acknowledgments
+* Examples and demos (highly recommended)
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