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