SDLC vs MDLC

The Software Development Life Cycle (SDLC) and the Model Development Life Cycle (MDLC) for AI are frameworks for building systems, but they differ significantly due to the unique nature of AI model development.

1. Objective and End Product

• SDLC: Focuses on building software applications, where the end product is a working, functional software solution. For example, SCRUM is a very popular SDLC based on Agile methodology.
• MDLC: Focuses on building machine learning (ML) or AI models, where the end product is an AI model that makes accurate predictions or classifications based on data.

2. Phases

• SDLC Phases: Requirement gathering, design, implementation (coding), testing, deployment, maintenance.
• MDLC Phases: Data collection, data cleaning and preprocessing, model development/selection, training, evaluation, deployment, monitoring and retraining.

3. Iterative Nature

• SDLC: While SDLC can follow iterative models, it’s generally more predictable.
• MDLC: Model development is highly experimental and iterative, involving continuous training, tuning, and re-evaluation.

4. Success Criteria

• SDLC: Success is typically determined by how well the software meets user requirements, performs without bugs, and satisfies functional specifications.
• MDLC: Success is measured by the model’s accuracy, precision, recall, F1 score, and overall generalization ability.

5. Tools and Technologies

• SDLC: Common tools include version control systems, IDEs, automated testing frameworks, and CI/CD pipelines.
• MDLC: Tools include frameworks like TensorFlow, PyTorch, and Scikit-learn, as well as data pipelines and experiment tracking tools. Data storage and GPU infrastructure are also crucial.

6. Post-Deployment

• SDLC: After deployment, software is maintained through updates, bug fixes, and feature additions.
• MDLC: Models require constant monitoring due to model drift. Retraining or fine-tuning is common to ensure continued accuracy.

7. Risk Management

• SDLC: Risks are managed through traditional quality assurance, software testing, and project management practices.
• MDLC: Risks include data quality, model bias, overfitting, and uncertainty of model behavior. Special care must be taken to manage biases and ensure the model generalizes well.

In summary, the SDLC focuses on delivering functional software, while the MDLC centers around developing, training, and maintaining AI models through an iterative, data-driven approach.

Check out this youtube video that explains it well.