Extra Reading

Functional Requirements

Functional requirements describe what the system should do. They define the specific behaviors, functions, or tasks the system needs to accomplish. For a Machine Learning (ML) system, functional requirements might include:

1. Input Data Requirements:

   • What data sources will be used?

   • What format will the data be in?

   • How often will the data be updated?

2. Output Specifications:

   • What form should the output take?

   • What actions or decisions should the system support?

3. Model Requirements:

   • What type of model is needed (e.g., classification, regression)?

   • What performance metrics are important (e.g., accuracy, precision, recall)?

4. Integration Points:

   • How will the ML system integrate with other systems or components?

   • What APIs or interfaces are required?

Non-Functional Requirements

Non-functional requirements describe how the system performs a task rather than the specific behaviors or functions. They include quality attributes, constraints, and performance measures. For an ML system, non-functional requirements might include:

1. Performance:

   • How quickly should the system produce results?

   • What are the latency requirements?

2. Scalability:

   • How should the system handle increasing amounts of data?

   • Can the system scale horizontally or vertically?

3. Reliability and Availability:

   • What is the expected uptime?

   • How will the system handle failures?

4. Security:

   • How will data be secured in transit and at rest?

   • What authentication and authorization mechanisms are needed?

5. Maintainability:

   • How easy is it to update the model?

   • What processes are in place for monitoring and improving model performance?

6. Compliance:

   • Are there any regulatory requirements that need to be met (e.g., GDPR)?

Driving the Conversation

Once you have clarified these requirements, you can structure the conversation around key aspects of ML system design:

1. Data Collection and Preparation:

   • Discuss the sources, volume, and quality of the data.

   • Outline the steps for data cleaning, transformation, and augmentation.

2. Feature Engineering:

   • Identify the key features that will drive model performance.

   • Discuss methods for feature extraction and selection.

3. Model Selection and Training:

   • Compare different algorithms and justify your choice.

   • Explain the training process, including any hyperparameter tuning.

4. Evaluation and Validation:

   • Define how the model's performance will be measured.

   • Discuss validation techniques such as cross-validation and A/B testing.

5. Deployment:

   • Describe the steps for deploying the model into a production environment.

   • Consider how the model will be monitored and maintained post-deployment.

By starting with a clear understanding of both functional and non-functional requirements, you can guide the discussion through the critical stages of ML system design. This approach ensures that you address all necessary aspects and demonstrate a thorough understanding of the entire ML lifecycle.