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.

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)

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

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