Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle
<|reference_start|>Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle: 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.<|reference_end|>
@article{garcia2024flow,
    title={Flow with FlorDB: Incremental Context Maintenance for the Machine
  Learning Lifecycle},
    author={Rolando Garcia, Pragya Kallanagoudar, Chithra Anand, Sarah E. Chasins,
  Joseph M. Hellerstein, Aditya G. Parameswaran},
    journal={arXiv preprint arXiv:2408.02498},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02498},
    primaryClass={cs.DB cs.SE}
}