This template serves as a roadmap that outlines key stages and best practices to guide an AI project toward a reliable production system.
Producers please note the need to adjust each step to suit the specific needs and context of your project.
Define the Problem & Objectives
- Business Case: Clearly articulate the business value and problem statement.
- Feasibility Study: Assess technical feasibility and align with business goals.
- Success Metrics: Define KPIs and model performance metrics (e.g., accuracy, latency).
2. Data Collection & Preparation
- Data Sourcing: Identify and integrate relevant data sources.
- Data Cleaning: Implement processes for handling missing, inconsistent, or noisy data.
- Feature Engineering: Transform raw data into features that enhance model performance.
- Data Governance: Establish policies for data privacy, security, and compliance.
3. Model Development & Validation
- Exploratory Data Analysis (EDA): Understand data distributions and relationships.
- Baseline Model: Develop a simple model as a performance benchmark.
- Model Selection: Choose algorithms that best suit the problem.
- Training & Tuning: Optimize hyperparameters and iterate on model architecture.
- Validation: Use cross-validation and test sets to ensure robust performance.
4. Development Environment & Experimentation
- Version Control: Use tools like Git for code management.
- Experiment Tracking: Implement tools (e.g., MLflow, Weights & Biases) to log experiments.
- Reproducibility: Ensure code, data, and environment dependencies are well-documented.
5. Deployment Architecture & Strategy
- Infrastructure Setup: Decide on cloud (AWS, Azure, GCP) or on-premises deployment.
- Containerization: Use Docker to encapsulate the model and its dependencies.
- Orchestration: Leverage Kubernetes or similar tools for scalable deployments.
- AI Management API & Model Serving: Deploy model as a service via RESTful APIs or gRPC.
6. Testing & Quality Assurance
- Unit & Integration Tests: Validate individual components and overall system integration.
- Performance Testing: Stress test the model under load and simulate production environments.
- Security & Compliance: Perform security audits and ensure regulatory compliance.
7. Monitoring & Maintenance
- Real-time Monitoring: Set up dashboards (e.g., Grafana, Prometheus) for model performance and system health.
- Data Drift & Model Decay: Monitor input data changes and retrain models as needed.
- Logging & Alerting: Implement logging mechanisms to capture errors and anomalies.
8. Documentation & Governance
- Technical Documentation: Maintain clear, detailed documentation on architecture, code, and processes.
- Operational Playbooks: Create runbooks for model updates, incident responses, and rollback procedures.
- Model Governance: Ensure ethical use, transparency, and auditability (e.g., bias assessments).
9. Continuous Improvement & Iteration
- Feedback Loop: Establish mechanisms for collecting feedback from end-users.
- Retraining Pipeline: Automate retraining based on performance metrics or data drift.
- Iterative Enhancement: Regularly review and update the system based on new insights or requirements.
10. Scaling & Optimization
- Resource Scaling: Plan for horizontal or vertical scaling to handle increased loads.
- Latency & Throughput Optimization: Optimize model inference times and API response rates.
- Cost Management: Monitor operational costs and optimize infrastructure spending.
Summary Checklist
- Problem definition and success metrics set
- Data pipeline established and validated
- Model trained, tuned, and evaluated
- Environment set up with version control and experiment tracking
- Deployment strategy defined and implemented
- Comprehensive testing, including security and performance
- Monitoring, logging, and retraining pipelines in place
- Full documentation and governance policies established
- Strategy for scaling and cost management defined