ML Reproducibility
ML reproducibility is the ability to replicate the ML workflow previously carried out and produce the same results as the original work.
What is Reproducibility?
Reproducibility refers to the ability to recreate or replicate. Machine learning reproducibility is replicating the ML workflow previously carried out in a paper, tutorial, and producing the same results as the original work.
Reproducibility is crucial from large-scale deployments perspectives. It also helps verify the effectiveness of research work and conclusions. It aids ML teams in reducing errors and ambiguity when models move from the development to the operation stage. The reproduced ML application ensures data consistency across ML pipelines and helps reduce any unintentional errors.
Reproducibility is a crucial tool to promote open research among tech communities. Trying reproducible ML experiments helps tech communities to access research findings, ideate new things, and convert ideas into reality.
The following tools are used to ensure ML reproducibility:
Data management and versioning: CML, DVC, delta Lake, Qri
Version Control: Github and Gitlab
Model versioning: DVC and MLFlow
Testing and validation: GCP and AWS
Analysis: Jupyter Notebook, JupyterLab, Google Colab
Reporting: Overleaf and Pandas
Open-source release: Binder
Challenges in achieving ML reproducibility
Changes in datasets
ML reproducibility experiments are mainly challenged by changes in datasets over time. Reaching the same conclusions is almost impossible when datasets change with the addition of new training data and data distribution variations.
No proper logging
ML reproducibility experiments demand appropriate logging of parameters changes - hyperparameter values, batch sizes, and more. Improper logging of such changes makes replicating difficult.
Changes in hyperparameters
Inconsistencies in hyperparameters during experimentation complicate driving expected results with reproducible ML experiments.
Changes in tools and frameworks
Machine learning is a rapidly evolving field. Frequent changes and updates in tools and utilities used to create the original work can affect ML experiments’ ability to produce previous results. Also, changing ML frameworks for on-paper work and reproducibility experiments can produce different results.
Change in the model environment
When the model moves from training to production, it moves from a controlled atmosphere to a more complex environment. For reproducible ML runs, it is necessary to capture versions, framework dependencies, hardware, and other environment parts.
Randomness
ML projects involve random initializations, data shuffling, and random argumentations. It adds complexity to reproducible ML initiatives.
Getting ML reproducibility right
Machine learning reproducibility is challenging to achieve, but its advent in ML research and large-scale deployments push ML professionals to explore this area more.
There is no single perfect toolkit or solution to try reproducible ML experiments, but the choice of tools depends on specific project needs. Integration of the following practices helps drive desired outcomes with these experiments.
- Checkpoint management
- Logging parameters
- Version control
- Data management and reporting
- Handling dependencies
- Training and validation
Although achieving ML reproducibility is challenging, ML research teams dare to take it for its business advantages, such as long-term project growth and faster results.
Further Reading
ML Reproducibility Tools and Best Practices
How to Solve Reproducibility in ML
The Importance of Reproducibility in Machine Learning Applications