AI Engineering

AMAH Daniel

Starting at

350

/wk

About this service

Summary

I specialize in delivering cutting-edge AI solutions tailored to your business needs, from custom AI models to seamless deployment and integration. What sets me apart is my hands-on expertise in Generative AI and AI engineering, combined with a focus on ethical AI practices, scalability, and client-centric support. I ensure not just a working solution, but a future-ready, maintainable, and impactful AI system that drives real value for your organization

Process

1. Discovery and Planning

Objective: Understand the client’s goals, requirements, and constraints.

Activities: Initial meetings, problem definition, scope finalization, and project planning.

2. Data Collection and Preparation

Objective: Gather and preprocess high-quality data for model training.

Activities: Data collection, cleaning, labeling, and augmentation.

3. Model Development

Objective: Design, train, and validate AI models.

Activities: Algorithm selection, model training, hyperparameter tuning, and validation.

4. Evaluation and Testing

Objective: Ensure the model meets performance and accuracy standards.

Activities: Performance testing, benchmarking, and error analysis.

5. Deployment

Objective: Integrate the model into the client’s environment.

Activities: Model packaging, API development, and deployment to cloud or on-premise systems.

6. Monitoring and Maintenance

Objective: Ensure the model performs well in production.

Activities: Real-time monitoring, performance tracking, and periodic updates.

7. Training and Handover

Objective: Empower the client to use and maintain the AI system.

Activities: User training, documentation, and post-deployment support.

What's included

Project Documentation
Comprehensive documentation including project goals, technical specifications, data sources, and model details.
Trained AI Model
Final AI model files (e.g., .pt, .h5, .pkl) ready for deployment, including saved weights and configurations.
Source Code Repository
Well-documented codebase with training, inference, and integration scripts, hosted on a version-controlled platform (e.g., GitHub).
Deployment Package
Deployment-ready scripts, containerized solutions (e.g., Docker), and cloud deployment guides for seamless integration.
Model Performance Report
Detailed evaluation metrics (e.g., accuracy, F1-score) and benchmarking results to demonstrate model effectiveness.
User Interface (if applicable)
Functional web/mobile app or API endpoints for easy interaction with the AI model.
Training and Support Materials
User manuals, training sessions, and post-deployment support to ensure smooth adoption and maintenance.
Ethical and Compliance Report
Documentation on bias mitigation, data privacy compliance, and model explainability.
Future Recommendations
Scalability plans, model improvement suggestions, and a roadmap for future iterations.
Optional: Custom AI Tools
Specialized tools or plugins tailored to the client’s specific use case.