# EdnaML: A Declarative API and Framework for Reproducible Deep Learning

arXiv: https://arxiv.org/abs/2211.06783 · HTML: https://mlsanity.com/paper/2211.06783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Theano: A Python framework for fast computation of mathematical expressions](https://mlsanity.com/paper/1605.02688/) (2016)
- [Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud](https://mlsanity.com/paper/2109.03285/) (2021)
- [Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles](https://mlsanity.com/paper/2006.12117/) (2020)
- [MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines](https://mlsanity.com/paper/2010.10246/) (2020)
- [Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering](https://mlsanity.com/paper/1910.04223/) (2019)
- [DataHub: Collaborative Data Science & Dataset Version Management at Scale](https://mlsanity.com/paper/1409.0798/) (2014)
- [ODIN: Automated Drift Detection and Recovery in Video Analytics](https://mlsanity.com/paper/2009.05440/) (2020)
- [MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection](https://mlsanity.com/paper/2205.09817/) (2022)
- [ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems](https://mlsanity.com/paper/2206.10110/) (2022)

## Built on by (most cited first, 1 of 1)

- [Continuously Reliable Detection of New-Normal Misinformation: Semantic Masking and Contrastive Smoothing in High-Density Latent Regions](https://mlsanity.com/paper/2301.07981/) (2023)

## Abstract (arXiv)

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.