  The metadata involved in integrating code, data, configuration, and feedback
into predictive models is varied and complex. This complexity is further
compounded by the agile development practices favored by data scientists and
machine learning engineers. These practices emphasize high experimentation
velocity and frequent deployments, which can make it challenging to keep track
of all the relevant metadata. The iterative nature of agile methods means that
models, datasets, and configurations are constantly evolving. Each experiment
might involve tweaks to the data preprocessing steps, changes in model
hyperparameters, or updates to the deployment environment. The need for rapid
iteration can lead to shortcuts or oversights in documentation and metadata
management. Effective metadata management requires robust yet flexible tools
and practices that can integrate and organize this information without slowing
down the development process. Traditional context management often emphasizes a
``metadata first'' approach, which can introduce significant friction for
developers. FlorDB reduces this friction through multiversion hindsight logging
and incremental context maintenance, allowing developers to add and refine
metadata after the fact. This ``metadata later'' approach enables a more
flexible and incremental development process, allowing data scientists to focus
on model creation and refinement without the burden of documentation upfront.
As shown in a demo, FlorDB can be used to build AI/ML applications with
integrated train-infer pipelines and managed feedback loops. Ultimately, the
goal of FlorDB is to ensure that critical metadata is maintained accurately and
efficiently, even in fast-paced agile workflows.
