Project RoadMap
Phase 1: Data Collection and Preparation (Month 1-2)
Define Data Requirements:
- Identify the specific data needed for the project, including customer demographics, transaction history, web interaction logs, marketing campaign data, and product catalog details.
- Collaborate with stakeholders to understand their data needs and ensure all relevant information is collected.
Data Source Identification and Access:
- Determine the internal and external data sources that will provide the required data.
- Ensure access to databases, data warehouses, and third-party data providers.
- Set up necessary data pipelines for continuous data flow if required.
Data Extraction:
- Extract data from identified sources using appropriate tools and techniques.
- Use SQL, APIs, web scraping, or ETL (Extract, Transform, Load) tools to gather the data.
- Ensure extraction processes are efficient and minimize disruptions to live systems.
Data Cleaning:
- Remove duplicates, correct errors, and handle missing values in the datasets.
- Standardize data formats and units to ensure consistency.
- Identify and resolve any discrepancies or anomalies in the data.
Data Quality Assurance:
- Conduct thorough quality checks to ensure the integrity and reliability of the data.
- Implement validation rules and perform audits to confirm data accuracy.
- Document the data cleaning and transformation processes for transparency and reproducibility.
Data Privacy and Security:
- Implement data encryption and access control mechanisms to protect sensitive information.
- Ensure compliance with relevant data protection regulations such as GDPR or CCPA.
- Anonymize or pseudonymize personal data where necessary to safeguard user privacy.
Phase 2: Model Development (Month 3-5)
Define Problem Statements:
- Clearly define the specific problems each model aims to solve (e.g., customer behavior prediction, sales forecasting, recommendation system).
Select Appropriate Algorithms:
- Research and select suitable machine learning algorithms for each problem statement.
- Consider a variety of models such as regression, classification, clustering, and recommendation algorithms.
Design Model Architecture:
- Design the architecture for each model, including input features, layers (for neural networks), and output formats.
- Document the rationale behind the chosen architectures.
Prepare Training and Validation Datasets:
- Split the preprocessed data into training, validation, and test sets.
- Ensure data splits maintain representative distributions and avoid data leakage.
Develop Model Training Pipelines:
- Implement training pipelines using selected machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
- Automate data preprocessing steps within the training pipelines.
Tools and Technologies:
- Machine Learning Frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
- Data Processing Tools (e.g., Pandas, NumPy)
- Model Training Infrastructure (e.g., GPUs, cloud computing resources)
- Version Control Systems (e.g., Git)
Phase 3: Integration and Deployment (Month 6-7)
Integration Planning and Initial Setup
- Develop Integration Plan
- Collaborate with Development Team
- Setup Development and Testing Environments
- API Development
Model Integration and Testing
- Integrate Models into the Platform
- Functional Testing
- User Interface Integration
- Security and Access Control
Deployment in Staging Environment
- Deploy Models in Staging
- Conduct Performance Testing
- User Acceptance Testing (UAT)
- Finalize Documentation
Production Deployment and Monitoring
- Final Review and Approval
- Production Deployment
- Monitoring and Maintenance Setup
- Post-Deployment Support
- Iterative Improvements
Phase 4: User Interface and Reporting (Month 8-9)
Requirements Gathering and Design Planning
- Stakeholder Interviews and Surveys
- Define UI/UX Requirements
- Design Mockups and Prototypes
- Review and Feedback
User Interface Development
- Front-End Development
- Integration with AI Models
- User Authentication and Access Control
- UI Testing
