From the course: The AI Ecosystem for Developers: Models, Datasets, and APIs
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Open-source vs. closed-source AI systems
From the course: The AI Ecosystem for Developers: Models, Datasets, and APIs
Open-source vs. closed-source AI systems
- [Instructor] By now, we have a solid foundation of the key components of the AI ecosystem, data, algorithms, models, frameworks, and computing infrastructure, and how they come together to form powerful AI systems and solutions. But how are these solutions made available, and what are the implications of the accessibility? Just like traditional software, AI systems can be categorized as open source or closed source, each with its own advantages, limitations, and implications for development, deployment, and ethical consideration. It is important to distinguish between the two as we'll be exploring both open source and closed source components throughout this course. An open source AI system is one where the code, model weights, training data or architecture are made publicly available, allowing users to study, modify, and improve upon the systems. Some of the key features of an open source AI system are transparency and collaboration. This means anyone can inspect, modify, and…
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