AI & ML Team
Building intelligent systems with machine learning and artificial intelligence
Expertise
- •Machine Learning Model Development
- •Natural Language Processing
- •Computer Vision & Image Recognition
- •Predictive Analytics
- •Model Training & Optimization
- •AI Integration & Deployment
Technologies
Our Process
Problem Definition & Data Collection
Understanding the AI problem and gathering training data
- →Define machine learning problem and success metrics
- →Identify data sources and collection methods
- →Gather and label training data
- →Assess data quality and completeness
- →Determine ethical considerations and biases
Data Preprocessing & Exploration
Preparing data for model training
- →Clean and preprocess raw data
- →Handle missing values and outliers
- →Perform exploratory data analysis (EDA)
- →Feature engineering and selection
- →Split data into training, validation, and test sets
Model Selection & Training
Choosing and training machine learning models
- →Select appropriate ML algorithms and architectures
- →Set up training pipeline and hyperparameters
- →Train models using training dataset
- →Monitor training metrics and loss curves
- →Perform hyperparameter tuning and optimization
Model Evaluation & Validation
Testing model performance and accuracy
- →Evaluate model on validation and test datasets
- →Calculate performance metrics (accuracy, precision, recall, F1)
- →Analyze model predictions and error cases
- →Perform cross-validation and statistical tests
- →Compare multiple models and select best performer
Model Deployment & Integration
Deploying AI models to production
- →Convert model to production-ready format
- →Create API endpoints for model inference
- →Optimize model for performance and latency
- →Implement model versioning and A/B testing
- →Integrate model with application backend
Monitoring & Maintenance
Ongoing model performance monitoring
- →Monitor model predictions and accuracy in production
- →Detect model drift and performance degradation
- →Collect new data for model retraining
- →Retrain and update models periodically
- →Document model performance and improvements
At least two team members have reviewed and approved the code changes
Code follows team coding standards, style guide, and best practices
ESLint/Prettier passes with zero errors and warnings
Complex logic is well-documented with clear comments and JSDoc
All console.log statements and debug code removed from production
Minimum 80% code coverage with meaningful unit tests
All integration tests pass successfully in CI/CD pipeline
Feature tested manually across different scenarios and edge cases
Verified functionality in Chrome, Firefox, Safari, and Edge
Tested on mobile devices (iOS/Android) and tablets
Existing features still work correctly after changes
All user inputs are validated and sanitized to prevent injection attacks
Proper authentication and authorization checks implemented
No API keys, passwords, or sensitive data exposed in code
All API calls use HTTPS and secure communication protocols
No critical or high-severity vulnerabilities in dependencies
Proper CORS and Content Security Policy configured
Page load time, API response time meet performance targets
Images optimized and compressed, using appropriate formats (WebP, AVIF)
Large components and routes are code-split and lazy-loaded
Database queries optimized with proper indexes and efficient joins
Appropriate caching (Redis, CDN) for static and dynamic content
JavaScript bundle size within acceptable limits (< 200KB gzipped)
Meets WCAG 2.1 Level AA accessibility standards
All interactive elements accessible via keyboard navigation
Tested with screen readers (NVDA, JAWS, VoiceOver)
Text and interactive elements meet minimum contrast ratios (4.5:1)
Proper ARIA labels and semantic HTML elements used
Clear focus indicators for all interactive elements
README.md includes setup instructions, dependencies, and usage
API endpoints documented with request/response examples
CHANGELOG.md updated with new features, fixes, and breaking changes
All required environment variables documented in .env.example
Deployment procedures documented for production release
Database migration scripts created and tested
Database backup completed before deployment
Rollback procedure documented and tested
Data validation and integrity checks implemented
All automated tests passing in CI/CD pipeline
Feature deployed and tested in staging environment
All production environment variables configured correctly
Error tracking and performance monitoring set up
Release notes prepared for stakeholder communication
Plan for verifying production deployment is successful