This repository serves as a production-grade template for scalable Artificial Intelligence and Machine Learning projects.

In the field of AI Engineering, the gap between a research notebook and a deployable system is often defined by structure, reproducibility, and separation of concerns. This repository implements a standardized architecture designed to bridge that gap, providing a robust foundation for:

• NLP & Large Language Models (LLM) pipelines (RAG, Fine-tuning).
• Computer Vision inference and training workflows.
• MLOps best practices including experiment tracking and modular codebases.

It allows engineers to move away from "spaghetti code" notebooks to maintainable, testable, and scalable AI systems.

Most AI projects fail to transition from experimentation to production.

Common issues include:

• Notebook-centric development with hidden state
• Tight coupling between data, models, and evaluation
• No clear path from training to deployment
• Lack of testing, reproducibility, and documentation

This repository was created to solve those problems at the architectural level.

It provides a repeatable, production-oriented blueprint for building AI systems that can evolve from research experiments into deployable, maintainable products.

graph TD
    A[Data Sources] --> B[data/raw]
    B --> C[ETL / Preprocessing]
    C --> D[data/processed]
    D --> E[Feature Engineering]
    E --> F[Model Training]
    F --> G[Evaluation & Validation]
    G --> H[Model Artifacts]
    H --> I[Deployment / Inference Layer]

This template enforces a strict separation between data, source code, and experiments.

yassinebenacha/
├── 📂 data/             # Data registry (immutable raw data vs processed artifacts)
├── 📂 notebooks/        # Jupyter notebooks for exploration (not for production code)
├── 📂 src/              # The core production codebase
│   ├── 📂 pipeline/     # Data processing & ETL pipelines
│   ├── 📂 models/       # Model definitions (PyTorch/Transformers classes)
│   └── 📂 utils/        # Shared utility functions
├── 📂 models/           # Serialized model weights & checkpoints
├── 📂 tests/            # Automated test suite (Pytest)
├── 📂 scripts/          # Standalone training/inference scripts
└── 📄 README.md         # Documentation & Entry point

• Separation of Concerns Data, models, training, and evaluation are strictly decoupled.

• Reproducibility First Every experiment should be repeatable from raw data to metrics.

• Scalability by Design Code structure supports growth from local experiments to cloud deployment.

• Framework Flexibility The template supports NLP, CV, and tabular ML use cases.

• Production Mindset Everything is designed with deployment, monitoring, and maintenance in mind.

flowchart TD
    A[1. Problem Definition] --> B[2. Data Ingestion]
    B --> C[3. Experimentation]
    C -->|Hypothesis Verified| D[4. Model Engineering]
    D --> E[5. Training & Eval]
    E -->|Metrics OK| F[6. Testing & Validation]
    F -->|Tests Pass| G[7. Deployment Readiness]

This template is designed to support the full AI engineering lifecycle:

1. Problem Definition

   • Clear separation between experimentation and production goals

2. Data Ingestion & Processing

   • Raw vs processed data separation
   • Reproducible preprocessing pipelines

3. Experimentation

   • Rapid prototyping in notebooks/
   • Feature exploration and hypothesis testing

4. Model Engineering

   • Modular model definitions in src/
   • Framework-agnostic design (PyTorch / Transformers)

5. Training & Evaluation

   • Script-driven training (not notebook execution)
   • Consistent evaluation logic

6. Testing & Validation

   • Unit and integration tests for critical components

7. Deployment Readiness

   • Clear path toward API or batch inference systems

graph LR
    Dev[Developer] -->|Push Code| Git[GitHub]
    Git -->|Trigger| CI[CI/CD Pipeline]
    CI -->|Run Tests| Test[Pytest]
    Test -->|Build| Docker[Docker Image]
    Docker -->|Deploy| Reg[Registry]
    
    style CI fill:#f96,stroke:#333,stroke-width:2px
    style Docker fill:#69f,stroke:#333,stroke-width:2px

This template is designed to integrate naturally with MLOps tooling, including:

• Experiment tracking (MLflow, Weights & Biases)
• Model versioning and artifact storage
• CI/CD pipelines (GitHub Actions)
• API deployment (FastAPI)
• Monitoring and retraining workflows

While not implemented by default, the structure is intentionally compatible with enterprise-grade AI pipelines.

To further elevate this template to a "deploy-anywhere" standard, the following improvements are planned:

• Containerization: Add Dockerfile and docker-compose.yml for reproducible environments.
• CI/CD Integration: Implement GitHub Actions for automated linting (ruff), testing (pytest), and building.
• Infrastructure as Code: Add Terraform scripts for cloud resource provisioning (AWS/Azure).
• Model Serving: Integrate TorchServe or Triton Inference Server examples.

To use this template for a new AI project:

git clone https://github.com/yassinebenacha/yassinebenacha.git
cd yassinebenacha

It is recommended to use a virtual environment or Conda.

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
# pip install -r requirements.txt (Populate with your project dependencies)

• Exploration: Start in notebooks/ for EDA (Exploratory Data Analysis).
• Engineering: Move stable logic to src/.
• Training: Execute training jobs via scripts/train.py (to be created).
• Testing: Validation via tests/.

If you are evaluating this repository for an AI Engineering or MLOps role, here is where to find key competence signals:

This repository serves as:

• A personal AI engineering standard
• A reusable base for future projects
• A reference for recruiters to understand my engineering approach

Concrete AI projects built on top of this template are linked separately and reuse this structure to ensure consistency and quality.

Yassine Ben Acha is an AI Engineer and final-year student at ENIAD, with a strong focus on Machine Learning, NLP, and production AI systems.

He has worked on industrial AI projects at Capgemini, where he contributed to intelligent diagnostic systems using NLP, RAG pipelines, and explainable AI. His interests lie at the intersection of AI research and real-world deployment.

🟢 Open for Opportunities: Yassine is currently seeking a 4-6 month internship starting January 2026.

• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn, XGBoost.
• NLP & LLMs: Hugging Face Transformers, RAG Pipelines, LangChain, Gemini API.
• MLOps & Engineering: Docker, FastAPI, Streamlit, Git/CI-CD.
• Visualization: Streamlit, Plotly, Matplotlib.

• Capgemini: Engineered an Intelligent Engine Fault Diagnosis System using NLP and RAG. Implemented Generative AI interfaces and focused on model explainability (SHAP).
• Prodigy Info Tech: Developed ML pipelines for regression and clustering tasks.
• Academic: Facial Recognition systems and Educational AI platforms.

• LinkedIn
• GitHub
• Portfolio

This project is open-source and available under the MIT License.