Your goal is to plan the project timeline for a new AI product.
Given a set of deliverables from your team members, organize them on the timeline to minimise the project deadline.
Drag deliverable cards from team members and drop them onto the timeline. Each card will show up as a colored bar representing its duration.
• Click and drag bars left or right to adjust their start date.
• Hover over a bar to reveal controls (for re-ordering or deleting a bar).
• Consider dependencies between team members!
• Pay attention to the requirements and outputs of each deliverable.
• Organize related tasks close to each other.
Data Engineer
Design and document the complete data pipeline architecture, including data flow diagrams, storage solutions, and processing frameworks.
Requires: Project requirements document, access to existing data sources, infrastructure specifications
Delivers: Comprehensive architecture document, infrastructure-as-code templates
Build and test the core ETL processes that will handle data ingestion, transformation, and loading.
Requires: Completed architecture design, development environment setup, sample data sets
Delivers: Working ETL pipeline, data quality reports, monitoring dashboard
Implement automated validation checks and data quality monitoring systems.
Requires: ETL pipeline completion, defined data quality metrics
Delivers: Validation test suite, quality monitoring tools, alert system
ML Engineer
Design and validate the ML model architecture, including model type selection and initial specifications.
Requires: Project requirements, processed dataset, performance targets
Delivers: Model architecture documentation, baseline implementation plan
Build and optimize the model training pipeline, including data preprocessing and augmentation.
Requires: Model architecture, processed training data, computational resources
Delivers: Training pipeline code, preprocessing modules, initial model results
Optimize model performance through hyperparameter tuning and architecture refinements.
Requires: Initial model training results, performance metrics, optimization targets
Delivers: Optimized model, performance analysis, optimization documentation
MLOps Engineer
Establish automated deployment pipelines for model and infrastructure updates.
Requires: Trained model, infrastructure access, deployment requirements
Delivers: Automated deployment pipeline, testing framework, deployment scripts
Implement comprehensive monitoring for model performance and system health.
Requires: Deployed model, defined KPIs, alerting requirements
Delivers: Monitoring dashboard, alert system, logging infrastructure
Develop and implement scaling solutions for production deployment.
Requires: Production deployment, performance metrics, scaling requirements
Delivers: Scaling configuration, load testing results, optimization report
AI Engineer
Design the overall AI system architecture, including integration points and system components.
Requires: Project requirements, infrastructure specifications, ML model specifications
Delivers: System architecture documentation, integration plan, technical specifications
Develop integration layers between ML models and production systems.
Requires: System architecture, ML models, API specifications
Delivers: Integration code, API documentation, performance tests
Optimize overall system performance and resource utilization.
Requires: Integrated system, performance metrics, optimization targets
Delivers: Optimized system, performance report, technical documentation
Prompt Engineer
Develop comprehensive prompt engineering strategy and guidelines.
Requires: Use case documentation, model capabilities, user requirements
Delivers: Prompt strategy document, best practices guide, template library
Create and test prompt templates for different use cases and user scenarios.
Requires: Prompt strategy, user feedback, testing framework
Delivers: Prompt library, testing results, optimization recommendations
Optimize prompts based on performance metrics and user feedback.
Requires: Initial prompts, performance data, user feedback
Delivers: Optimized prompts, performance analysis, documentation
AI Ethics Specialist
Develop comprehensive ethics guidelines and assessment framework.
Requires: Project scope, stakeholder input, regulatory requirements
Delivers: Ethics guidelines, assessment checklist, compliance framework
Create and implement bias testing methodologies and monitoring systems.
Requires: Initial model results, training data analysis, fairness metrics
Delivers: Bias testing tools, evaluation reports, mitigation recommendations
Develop systems for model explanability and decision transparency.
Requires: Working model, stakeholder requirements, regulatory guidelines
Delivers: Explanation system, documentation templates, communication guidelines