Why Reproducibility is Important for ML

Reproducibility — the ability to replicate an experiment and obtain the same results by using the same methodology — is critical in all fields of science. It’s also important in artificial intelligence (AI) and machine learning (ML) applications.
Alon Lev
Alon Lev
Co-Founder & CEO at Qwak
November 3, 2022
Table of contents
Why Reproducibility is Important for ML

Reproducibility — the ability to replicate an experiment and obtain the same results by using the same methodology — is critical in all fields of science. It’s also important in artificial intelligence (AI) and machine learning (ML) applications.

In a perfect world, the inner workings of an ML system would be completely transparent. As any experienced ML practitioner will tell you, however, it’s not always clear if an ML project is reproducible. 

What is reproducibility in ML?

Reproducibility in AI and ML means that you can repeatedly run your algorithm on certain datasets and obtain the same, or very similar, results on a particular project. This process encompasses design, reporting, data analysis, and interpretation. 

Reproducibility is critical for both ML research and applications for two reasons:

  • ML research — In terms of research, it’s critical because scientific progress depends on the ability of independent researchers to scrutinize and reproduce the results of a study. ML cannot be improved or applied in other areas if the essential components aren’t documented or reproducible.
  • ML applications — In terms of applications in business, reproducibility enables the building of systems that are less prone to errors and are generally more reliable and predictable. This is important for convincing decision-makers to scale AI systems and enable more users to benefit from them.

In essence, being able to replicate results is very important, as it means a project is scalable and ready to move for production with large-scale deployment. Reproducibility can effectively be boiled down to these core ML model elements: code, data, model parameters, and environment. 

  • Code — To achieve reproducibility, you must track and record changes in code and algorithms during experimentation. 
  • Data — Adding new datasets, data distribution, and sample changes will impact the outcome of a model. As such, dataset versioning and change tracking must be carried out. 
  • Environment — If a project is to be reproducible, the environment it was produced in must be captured. Framework dependencies, versions, the hardware used, and everything else must be logged and easy to reproduce. 
  • Model parameters — Parameters such as hyperparameters and seeds also need to be reproduced accurately because they wield a great deal of control over the learning process.  

Despite the importance of reproducibility, less than one-third of AI research is reproducible, and only around 5% of AI researchers share source code. In addition, fewer than a third share test data in research papers. This is often referred to as the ‘reproducibility crisis’ in AI and ML. 

Reproducibility challenges in machine learning

Now that we know what reproducibility is, let’s take a look at some of the most common reproducibility challenges in machine learning environments. 

A lack of records

By far the biggest challenge to reproducible experiments in ML is a lack of records. When ML teams fail to record inputs and new decisions, it makes it much more difficult to replicate the results that have been achieved. 

During experimentation, parameters such as hyperparameter values and batch sizes change. Without properly logging the changes in these parameters, it becomes difficult to understand and replicate the model.

Changes in data

It’s pretty much impossible to get the same result(s) when the data on the original work has been changed. For example, when new training data is added to a dataset after certain results have been achieved, it’s next to impossible to get the same result.

In addition, incorrect data transformations (i.e., cleaning) on a dataset and changes in data distribution can also hamper reproducibility. 

Changes to ML frameworks

It’s no secret that ML frameworks and libraries are always being updated and changed. A specific library version that was used to generate a particular result last week might no longer be available when you need it, and this can influence the result. 

As an example, PyTorch 1.7+ supports mixed precision natively from the apex library from NVIDIA whereas previous versions didn’t offer this. On the subject of PyTorch, changing from one framework (i.e., PyTorch) to another, such as TensorFlow, will also generate different results. 

Experimentation and randomization

Machine learning is experimental. Many iterations go into developing a working model. Changes in algorithms, data, environments, and parameters are part and parcel of the ML development process, and with this comes the difficulty of losing important details.

ML is also full of randomization, especially in projects where lots of randomizations happen, such as random initializations, random noise introductions, and random augmentations. This can also hinder reproducibility. 

Improve the reproducibility of your ML models with MLOps

The best way to improve the reproducibility of your ML models is by making use of MLOps best practices and tools. 

MLOps is a core function of machine learning engineering that is focused on streamlining the process of deploying ML models into production and then monitoring and maintaining them once there. Generally speaking, MLOps involves streamlining AI and ML lifecycles with automation and a unified framework within an organization.

Some of the MLOps tools that help to improve reproducibility include: 

  • Experiment tracking — Developing ML models is an iterative process where practitioners experiment with different model components. Tracking tools help to keep tabs on important information about these experiments in a structured and digestible manner. 
  • Data lineage — This helps to keep track of where data originates, what happens to it, and where it goes over the data lifecycle. 
  • Data versioning — AI systems are often trained on dynamic datasets that reflect environmental changes. Versioning tools help ML teams to store different versions of data that were created or changed at certain points in time.
  • Model versioning — Similarly, data versioning tools help keep track of different versions of ML models with different model types, parameters, and hyperparameters, and enable ML teams to compare them.
  • Feature stores — Features are attributes of training data that are relevant to the problem that you’re trying to solve with an ML model. After feature engineering, feature stores standardize and store different features for reuse later. 

Achieve better reproducibility with MLOps

Reproducibility is the key to better data science, and ML research, it’s what makes your project flexible, and perfect for large-scale production. And the key to reproducibility is robust MLOps. 

MLOps is a vital process that sits at the core of pretty much every ML market leader today, largely because it’s all about the implementation of a comprehensive system that enables machine learning teams to optimize and drive continuous improvement in their ML environments, from development to deployment and beyond.

To choose the right MLOps platform and tools, it is important that ML teams understand not just the organization’s mission and long-term goals but also its current data science environment and the value that MLOps could deliver.

Qwak is the full-service machine learning platform that enables teams to take their models and transform them into well-engineered products. Our cloud-based platform removes the friction from ML development and deployment while enabling fast iterations, limitless scaling, and customizable infrastructure. 

‍Want to find out more about how Qwak could help you deploy your ML models effectively and efficiently? Get in touch for your free demo! 

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