Data Collection & Preprocessing
- Gather structured and unstructured data from various sources.
- Perform data cleansing, feature engineering, and transformation.
- Ensure data privacy, security, and regulatory compliance..
Model Development & Training
- Select the best AI/ML algorithms based on project needs.
- Train models using supervised, unsupervised, or reinforcement learning.
- Optimize model accuracy and eliminate biases.
Model Deployment & Integration
- Deploy AI models into business applications, cloud, or on-premises systems.
- Conduct real-time testing, validation, and performance benchmarking.
- Ensure seamless API integration with existing infrastructure.
Monitoring & Optimization
- Continuously monitor AI model performance.
- Retrain models with new data for accuracy improvement.
- Adapt AI solutions to evolving business needs.