  Artificial Intelligence (AI) development is inherently iterative and
experimental. Over the course of normal development, especially with the advent
of automated AI, hundreds or thousands of experiments are generated and are
often lost or never examined again. There is a lost opportunity to document
these experiments and learn from them at scale, but the complexity of tracking
and reproducing these experiments is often prohibitive to data scientists. We
present the Lifelong Database of Experiments (LDE) that automatically extracts
and stores linked metadata from experiment artifacts and provides features to
reproduce these artifacts and perform meta-learning across them. We store
context from multiple stages of the AI development lifecycle including
datasets, pipelines, how each is configured, and training runs with information
about their runtime environment. The standardized nature of the stored metadata
allows for querying and aggregation, especially in terms of ranking artifacts
by performance metrics. We exhibit the capabilities of the LDE by reproducing
an existing meta-learning study and storing the reproduced metadata in our
system. Then, we perform two experiments on this metadata: 1) examining the
reproducibility and variability of the performance metrics and 2) implementing
a number of meta-learning algorithms on top of the data and examining how
variability in experimental results impacts recommendation performance. The
experimental results suggest significant variation in performance, especially
depending on dataset configurations; this variation carries over when
meta-learning is built on top of the results, with performance improving when
using aggregated results. This suggests that a system that automatically
collects and aggregates results such as the LDE not only assists in
implementing meta-learning but may also improve its performance.
