4. Solution Strategy

The Solution Strategy defines the fundamental design decisions, technology choices, and best practices that guide the development of the MLOps pipeline. These decisions ensure that the system meets technical, organizational, and functional requirements effectively.

4.1 Guiding Principles

The project follows these key principles:

• Reproducibility: The entire pipeline (from data processing to deployment) must be easily reproducible across different environments.

• Scalability: The system should handle increasing data loads and future model iterations without significant rework.

• Automation: Continuous Integration and Continuous Deployment (CI/CD) should minimize manual interventions and improve efficiency.

• Modularity: Each component (data processing, model training, API, UI) is developed independently, ensuring flexibility and maintainability.

• Open Source & Transparency: The project is released under GPLv3, ensuring community collaboration and knowledge sharing.

4.2 Architectural Approach

• Microservices-Based Architecture

  • The system is split into independent services, such as FastAPI (backend), Streamlit (frontend) and a separate data-processor.

  • This allows independent scaling and development.

• Cloud & Local Compatibility

  • The system should be deployable both on local machines and on cloud platform such as Azure, AWS or GCP. It should be also possible to run the system on a own server.

• Data & Model Versioning

  • MinIO (object storage) is used to store processed or raw datasets for example. It should maintain different dataset versions.

• Docker

  • Docker ensures reproducible infrastructure deployment via a sing docker-compose file.

4.3 Technology Stack

Component	Technology	Justification
Programming Language	Python	Wide ML & MLOps support
Model Training & Tracking	Scikit-learn and MLflow	Standard ML pipelines
Data Processing (Backend)	Pandas, NumPy …	Efficient data handling
Version Control	Git (GitHub)	One of the standards
Infrastructure	Docker	Scalable & reproducible deployments
Frontend	Streamlit	Lightweight UI for ML apps
Backend	FastAPI	Fast and scalable API, also easy to develop
CI/CD	Github Actions	Automated testing & deployment
Logging	Loki	Collects logs from all services (FastAPI, Streamlit, MLflow, etc.)
Visualization and Monitoring	Grafana	Displays logs, metrics, and traces from Loki, Tempo, and Mimir, also triggers the threshold
Distributed Tracing	Tempo	Captures request traces across multiple services for debugging
Metrics storage	Mimir	Stores system metrics like CPU, memory, request latency, etc.

4.4 Risk Management

Risk	Mitigation Strategy
Incompatibility across environments	Docker ensures consistency
Data/model loss	MinIO & MLflow for versioning