Introduction

At Afriq Silicon, we believe powerful technology should be accessible to everyone — not just those with a technical background. Our work with the African Centre for Technology Studies (ACTS) on ACTS ML puts that belief into practice.

ACTS ML is a free, no-code machine learning platform built for researchers, students, farmers, health workers, and community practitioners across Africa who have data and questions, but no coding experience.

Project Overview

ACTS ML: No-Code Machine Learning for Africa

ACTS ML lets any user upload a dataset, clean and prepare it visually, build a training workflow using a drag-and-drop builder, and get results — all without writing a single line of code. Everything is organised into projects, giving researchers a single place for their data, models, and outcomes.

Technology Stack

Leveraging the Right Tools

• Frontend: Vue 3 for a fast, responsive interface
• Backend: Directus and FastAPI for data management and ML logic
• Infrastructure: Kubernetes for scalable model training jobs
• ML: scikit-learn, PyTorch, and ONNX export support
• Storage: MinIO / S3 for datasets and model artifacts
• Design: Figma for an intuitive, non-technical user experience

Features and Functionality

Key Features

• Project-based organisation: Keep datasets, workflows, and results in one place
• Data studio: Visual tools for cleaning, preprocessing, and profiling data
• Drag-and-drop workflow builder: Assemble training pipelines without code
• Open data sharing: Mark datasets as open source for community reuse
• Multiple ML tasks: Classification, regression, and clustering supported
• Reproducibility: Every experiment is versioned and traceable

Development Process

Agile, User-Centred Development

The platform was built with non-technical users at the centre of every decision. We followed an iterative process with continuous feedback from ACTS researchers, ensuring the interface remained simple without sacrificing depth for users who need it.

Conclusion

Looking Ahead

ACTS ML is a step toward a future where geography and technical background are no longer barriers to doing meaningful data science. We are proud to have partnered with ACTS on a platform that expands who gets to participate in machine learning — and we look forward to seeing what Africa's researchers, students, and innovators build with it.