Agile Development Optimization for AI Projects

# Traditional User Story
As a user, I want to receive personalized recommendations 
so that I can discover relevant products.

# AI-Optimized Hypothesis Story
**Hypothesis:** Using collaborative filtering with user behavior data 
will improve recommendation relevance by 15% compared to the current rule-based system.

**Success Criteria:**
- Click-through rate increases by 15%
- User engagement time increases by 10%
- Model accuracy > 85% on validation set

**Experiment Design:**
- A/B test with 20% of users
- 2-week testing period
- Fallback to current system if performance degrades

# Daily Standup Structure for AI Teams

## Standard Questions:
1. What did you complete yesterday?
2. What are you working on today?
3. What blockers do you have?

## AI-Specific Additions:
4. What experiments are running and when will results be available?
5. Any data quality issues discovered?
6. Model performance updates or concerns?
7. Infrastructure or compute resource needs?

## Weekly Deep Dive:
- Model performance review
- Data drift analysis
- Experiment results discussion
- Technical debt assessment

import mlflow
import wandb
from jira import JIRA

class ExperimentTracker:
    def __init__(self, jira_ticket, experiment_name):
        self.jira_ticket = jira_ticket
        self.experiment_name = experiment_name
        
        # Initialize tracking
        mlflow.start_run(run_name=f"{jira_ticket}_{experiment_name}")
        wandb.init(project="ai-project", name=experiment_name, 
                  tags=[jira_ticket])
    
    def log_hypothesis(self, hypothesis, success_criteria):
        mlflow.log_param("hypothesis", hypothesis)
        mlflow.log_param("success_criteria", success_criteria)
        wandb.config.update({
            "hypothesis": hypothesis,
            "success_criteria": success_criteria
        })
    
    def log_results(self, metrics, conclusion):
        for key, value in metrics.items():
            mlflow.log_metric(key, value)
            wandb.log({key: value})
        
        mlflow.log_param("conclusion", conclusion)
        
        # Update JIRA ticket with results
        self.update_jira_ticket(metrics, conclusion)
    
    def update_jira_ticket(self, metrics, conclusion):
        # Update JIRA ticket with experiment results
        comment = f"""
        Experiment Results:
        {metrics}
        
        Conclusion: {conclusion}
        
        MLflow Run: {mlflow.active_run().info.run_id}
        """
        # Add comment to JIRA ticket

# Post-Experiment Review Template

## Experiment Summary
- **Hypothesis:** [Original hypothesis]
- **Expected Outcome:** [What we expected to happen]
- **Actual Outcome:** [What actually happened]

## Analysis
- **Why did it fail?** [Root cause analysis]
- **What did we learn?** [Key insights gained]
- **What would we do differently?** [Process improvements]

## Next Steps
- **Immediate actions:** [What to do now]
- **Future experiments:** [What to try next]
- **Process changes:** [How to improve our approach]

## Knowledge Sharing
- **Team presentation:** [Date and key points]
- **Documentation updates:** [What to document]
- **Best practices:** [New guidelines to follow]