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content/page/Evaluation Rubric - Training, Evaluating, and Deploying ML Models Portfolio Project.md

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+# Evaluation Rubric for Portfolio Project
+
+Candidates are to submit a public Github repo of an ML project built mainly on Python. This will be evaluated using the following rubric. Candidates must explain their design choices in a one-to-one 30 min interview after the project has been graded and accepted. We encourage candidates to prepare a brief powerpoint presentation for this (15 slides). 
+
+The minimum grade to pass the portfolio project is 70 points. 
+
+1. **Data Preparation and Preprocessing (10 points)**:
+   - How effectively is the data cleaned, normalized, and preprocessed?
+   - Are techniques like handling missing data, normalization, and feature engineering appropriately applied?
+   - Is there a thoughtful approach to dealing with imbalanced data or outliers?
+
+2. **Model Selection and Rationale (10 points)**:
+   - Is the choice of model suitable for the problem at hand?
+   - How well is the reasoning for selecting a particular model articulated?
+   - Are comparisons made with alternative models?
+
+3. **Model Training and Validation (10 points)**:
+   - How effectively is the model trained and validated?
+   - Are appropriate metrics chosen for evaluating model performance?
+   - Is there a robust approach to training, such as cross-validation or use of a validation set?
+
+4. **Code Quality and Efficiency (10 points)**:
+   - Is the code well-organized, readable, and efficient?
+   - Are best practices in coding and software engineering followed?
+   - How are error handling and exception management implemented?
+
+5. **API Design and Implementation (10 points)**:
+   - How well is the REST API designed (endpoints, request-response structure)?
+   - Are best practices in API development (like security, scalability) considered?
+   - Is there proper documentation for the API (e.g., Swagger documentation)?
+
+6. **Model Deployment and Environment (10 points)**:
+   - How effectively is the model deployed for use via the REST endpoint?
+   - Are considerations like load balancing, scalability, and environment stability addressed?
+   - Is there an effective use of cloud services or containerization (e.g., Docker)?
+
+7. **Integration of Machine Learning and API (10 points)**:
+   - How well are the machine learning model and REST API integrated?
+   - Is there efficient handling of requests and responses between the server and the model?
+   - Are there measures for performance optimization in the integration?
+
+8. **Security and Data Privacy (10 points)**:
+   - Are security best practices for APIs and machine learning models implemented?
+   - How is data privacy and protection handled, especially with sensitive data?
+   - Are there mechanisms to prevent common vulnerabilities (e.g., SQL injection, data leaks)?
+
+9. **Testing and Reliability (10 points)**:
+   - How thoroughly is the system (both the model and API) tested?
+   - Are there unit tests, integration tests, and system tests?
+   - Is there evidence of reliable and consistent performance under different scenarios?
+
+10. **Documentation, Reporting, and Usability (10 points)**:
+    - Is the project well-documented, including model training, API usage, and deployment details?
+    - Are the results, challenges, and decision-making processes clearly communicated?
+    - Is the API user-friendly and easy to use for the end-users?
+

content/page/Rubric - Interview for Training, Evaluating, and Deploying ML Models.md

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+
+# Evaluation Rubric for Project Interview
+
+**Discussion Point 1: Problem Definition and Motivation (40 points)**
+
+- **Problem Definition (15 points):** 
+  - Clear articulation of the problem statement.
+  - Identification of stakeholders and target audience.
+  - Justification of why solving this problem is important or relevant.
+
+- **Motivation (15 points):** 
+  - Explanation of the context or background leading to the problem statement.
+  - Discussion of potential impact or benefits of solving the problem.
+  - Demonstration of understanding of the problem's significance in its respective domain.
+
+- **Metrics Alignment (10 points):** 
+  - Selection and justification of appropriate evaluation metrics.
+  - Explanation of how chosen metrics align with the problem statement and desired outcomes.
+  - Consideration of potential limitations or biases in chosen metrics.
+
+**Discussion Point 2: Techniques Used and Code Implementation (40 points)**
+
+- **Technique Description (20 points):** 
+  - Comprehensive explanation of the machine learning techniques employed.
+  - Discussion of why these techniques were chosen over alternatives.
+  - Description of any modifications or customizations made to suit the problem.
+
+- **Code Implementation (20 points):** 
+  - Clear demonstration of how the techniques were implemented in code.
+  - Well-organized and documented codebase.
+  - Consideration of best practices in code structure, readability, and efficiency.
+
+**Discussion Point 3: Presentation Clarity (20 points)**
+
+- **Google Slides (10 points):** 
+  - Effective use of Google Slides as a presentation medium.
+  - Clarity of visual aids, if any, used in the presentation.
+  - Consistency and coherence in slide design and layout.
+
+- **Structure and Delivery (10 points):** 
+  - Clear and concise introduction setting the stage for the presentation.
+  - Well-defined problem statement that captures the audience's attention.
+  - Coherent conclusions summarizing key findings and insights.
+  - Insightful discussion of potential avenues for future work or improvement.
+
+#### References:
+1. [ML Model Evaluation and Selection](https://neptune.ai/blog/ml-model-evaluation-and-selection)
+2. [Best Practices for Code Documentation in Python](https://realpython.com/documenting-python-code/)
+3. [Tips for Effective Presentation Design](https://www.youtube.com/watch?v=4TQC83nGv4Y)
+4. [How to Enhance your Machine Learning Presentation with Visuals and Stories](https://www.linkedin.com/advice/0/how-do-you-enhance-your-machine-learning-presentation)
+
+## Example Presentations
+* [Bike Map - Using Computer Vision for Bike Routing](https://www.youtube.com/watch?v=nNMmz6Ei9Qg)
+* [AI Font Generator](https://www.youtube.com/watch?v=nNMmz6Ei9Qg)
+* [Art Speak Simplifier](https://www.youtube.com/watch?v=laG4MiNRX54)
+* [Career Changer App](https://www.youtube.com/watch?v=laG4MiNRX54)
+* [Roof Segmentation for Efficient Solar Panel Placement](https://www.youtube.com/watch?v=8tBwanAYA90)