JSON (JavaScript Object Notation)
JSON is a lightweight, text-based format for structuring data. It emerged from JavaScript but quickly spread far beyond its origins to become a universal standard. Developers appreciate JSON because it strikes the right balance between simplicity and expressiveness: easy for humans to read, easy for machines to parse.
In AI and Machine Learning, JSON is everywhere. When training models, JSON files are often used to define configuration settings—batch sizes, learning rates, optimizer types. During experiments, logs are frequently written in JSON, allowing data scientists to track performance across epochs in a structured way. And in deployment, APIs typically send and receive JSON objects, making it the “lingua franca” of AI-powered web services.
A practical example can be seen in computer vision: annotations for object detection datasets such as COCO (Common Objects in Context) are distributed as JSON files. Each entry specifies the image, bounding box coordinates, segmentation masks, and class labels. Without a format like JSON, coordinating datasets across researchers would be significantly harder.
Still, JSON has limitations. For massive datasets or high-frequency systems, parsing JSON can be slower and more memory-intensive than using compact binary encodings. That’s why some AI production pipelines prefer formats like Protocol Buffers or Avro.
JSON has become so ubiquitous that many developers don’t even think of it as a “format” anymore—it’s just the default way to pass data around. Its key–value structure makes it versatile enough to describe anything from a single configuration parameter to a deeply nested dataset. Because it is language-independent, nearly every programming language offers native libraries for parsing and generating JSON, making interoperability one of its biggest strengths.
In AI workflows, JSON is also key for experiment reproducibility. By saving hyperparameters, random seeds, and preprocessing steps in JSON configs, researchers ensure that results can be replicated later. This is particularly valuable in large teams, where consistent documentation is as important as the models themselves.
On the flip side, JSON’s verbosity can become an issue when scaling. Large annotation files with millions of entries can reach gigabytes in size, straining storage and I/O performance. For production systems requiring speed, formats like MessagePack or Protocol Buffers often replace JSON behind the scenes, though JSON is still preferred for debugging and human readability.
🔗 References:
- JSON.org
- Lin et al. (2014), Microsoft COCO: Common Objects in Context
