  Machine Learning has become the bedrock of recent advances in text, image,
video, and audio processing and generation. Most production systems deal with
several models during deployment and training, each with a variety of tuned
hyperparameters. Furthermore, data collection and processing aspects of ML
pipelines are receiving increasing interest due to their importance in creating
sustainable high-quality classifiers. We present EdnaML, a framework with a
declarative API for reproducible deep learning. EdnaML provides low-level
building blocks that can be composed manually, as well as a high-level pipeline
orchestration API to automate data collection, data processing, classifier
training, classifier deployment, and model monitoring. Our layered API allows
users to manage ML pipelines at high-level component abstractions, while
providing flexibility to modify any part of it through the building blocks. We
present several examples of ML pipelines with EdnaML, including a large-scale
fake news labeling and classification system with six sub-pipelines managed by
EdnaML.
