Versioning & Experiment Tracking

AI development is inherently experimental. You try different model architectures, training datasets, hyperparameters, and feature sets, and you need to keep track of what you tried, what worked, and why. Experiment tracking tools like MLflow, Weights & Biases, Neptune, and Comet ML record the parameters, metrics, artefacts, and code associated with each experiment. A good experiment tracking system lets you compare runs side by side, reproduce earlier results, and understand which changes led to improvements. Without it, teams rely on spreadsheets, memory, and naming conventions that inevitably break down as the number of experiments grows. Versioning in the ML context goes beyond code to encompass data, model artefacts, configurations, and environment specifications. Each of these can change independently, and you need to track them all to have a complete picture of what produced a given model. The combination of experiment tracking and comprehensive versioning creates an audit trail that's valuable for debugging, compliance, and knowledge sharing. When a new team member asks "why did we choose this model architecture?" or a regulator asks "what data was this model trained on?", you can answer with specifics rather than vague recollections. Investing in these practices early saves significant time and frustration as your AI efforts mature.