Data Versioning in ML

Highlights –

• Better training data is ensured by data versioning, which includes quick experimentation, iteration, and training models on data.
• Data versioning can be done in two main ways – file versioning and using a tool to track changes to data.

With technological advancements, companies are relying on new models to make business decisions. Businesses are using AI/ML models to make strategic decisions, but high-quality data is a sine qua non to make accurate predictions using these models. This is why data is referred to as the new oil because successful businesses need to have their refinery.

The need for data is felt everywhere – be it in the form of documentation or source code. Data is more significant today as it helps users gain an understanding of everything that is going on.

But getting high-quality data is not an easy task. New adjustments need to be made to data to make them better and in sync with current requirements. Consequently, every piece of data undergoes a change in either structure, content, or condition. Essentially, high-quality information is added to old data, which is reprocessed and corrected to ensure the dataset version is up-to-date with current times. This is what is referred to as data versioning.

Although AI/ML methods frequently include data and models when creating business applications, the ecosystem cannot scale rapidly if data, parameters, and model iterations are not adequately managed. It involves numerous crucial processes, one of which is versioning the data. The data versioning system serves as the platform’s backbone as the practice expands.

Using Data Versioning in Machine Learning

Data versioning in machine learning simply means educating the machine to differentiate between various kinds of data. When a huge number of data samples of similar types are collected and processed, every step taken by the machine becomes more efficient. When large amounts of data, such as image recognition systems, needs to be worked upon, it is essential to carry out code updates to the ML algorithm so that they get accustomed to new techniques for photo identification. This can be done via data versioning, which is used by many devices.

Necessarily, data versioning can also train or alter an algorithm, enabling it to identify and learn new strategies while also recognizing different elements. Data versioning is especially crucial for your workflow if you care about repeatability, traceability, and the lineage of your ML models. With data versioning, one can develop an artifact version, such as a hash of a dataset or model, which can help gauge and compare the artifact later in the process. Oftentimes, one would enter this data version into the metadata management solution to crosscheck that the model training was versioned and repeatable.

And when many people work on the same code, it also safeguards the code from some unintended changes. A developer is accountable for several questions when building a machine learning model: Which datasets are used for model training? What frameworks are being used to control the learning process of the machines? Which pipeline is being used to create the model? What is the version of the previously deployed model? All of these call for version control in ML models.

Benefits of data versioning in ML

Ensures better training of data: ML incorporates quick experimentation, iteration, and training models on data. Therefore, training on inaccurate data might have a detrimental impact on the outcomes of an ML project.

Helps track data schema: Enterprise data is typically acquired in batches, and small modifications to the ML schema are frequently made over the course of a project. The data schema can be readily tracked and improved over time with proper versioning. Additionally, you can determine if these changes are backward and forward-compatible.

Conduct continual model training: With data being refreshed periodically in production environments, it may cause a fresh run of the model training pipeline. Data versioning is crucial in such times when automated retraining occurs as it helps track the model’s efficacy.

Strengthen traceability and reproducibility: Data scientists must be able to track, identify the source of data, and specify which version of a dataset supports their research findings. They have to rerun the complete ML pipeline and replicate the precise results each time since it is a vital input for the modeling process. Consequently, the original training data must always be accessible. Developers can just reload the old dataset in the pipeline, which saves potential business loss. So, from the perspective of reproducibility and traceability, appropriate versioning is essential.

Auditing: Proper versioning allows one to uphold the integrity of data-based activities by tracking when the changes are performed. By watching and examining the actions of both users and models, auditors can spot deliberate and unintentional errors in user behavior. As a result, data science auditors may assess the impact of data modifications on model correctness and decide on the best ML procedures for the company.

Formats of versioning

While there is no universally accepted methodology for versioning data, the following formats are prevalent:

The three-part semantic version number convention is the most common format to show different versions. For example, 5.1.7 shows a specific version of data. The left-hand number (5) denotes significant differences between the two versions of the data. The number in the middle (1) shows new features that are compatible with the old ones, and the number on the right (7) shows minor bug fixes compared to older versions.

It is also possible to name data versions based on their status. To cite an example, a dataset could be either incomplete or complete, filtered or not, cleaned or not, etc. This information can be useful for practitioners, mainly when they use a cloud system to work together on a dataset.

Data versions can be named based on the most recent process to which they were exposed. For example, normalized or changed based on something, and so on.

Other Options to version the data

Data versioning can be done in two main ways. You can either use file versioning or build a software system to meet the data engineering needs of your company.

File versioning

One way to handle data versions is to save them manually on your computer. File versioning is helpful when:

• Small businesses with only a few data engineers or scientists working together.
• If data contains particularly sensitive information, only a few executives and data engineers should be able to see and understand it.
• If the task isn’t suitable for teamwork, which is when different people can’t work together to reach a common goal.

Using a tool to track changes to data

Specialized tools offer a substitute to file versioning. One can make the software in-house or hire someone else to do it.

Data versioning tools work better for businesses that:

• Using a specialized tool is more efficient when more than one person is working on a dataset. This is because file versioning doesn’t let a group of people simultaneously work on the same file.
• Using the software is better than file versioning when people need to work together from different places.
• It is possible to figure out where and who made a mistake using data versioning software. So, the accountability of the team increases.

Challenges to data versioning

Versioning data takes up storage space, causes data security problems, and since cloud software systems are so popular, it is hard to choose the right provider.

Limited storage

Data is saved in a different version each time, so more storage space is needed. Companies that produce or use large amounts of data would have to pay more money and that too for many versions of the data. Companies must find the right balance between the benefits of versioning and the costs of storing it.

Security worries

Businesses need to make sure their data is secure to protect their reputation. But the risk of data loss or leakage increases as more data versions is stored. This risk is even bigger for people who use the cloud because they outsource their IT work, which gives them less control over their data. Organizations must evaluate and understand this risk to determine how to version data.

Picking the right service provider

If you decide to use a data versioning tool, you should pick the one that best fits your business needs. Different cloud service providers have different features and prices. So, if you want to ensure your cloud costs are optimized, you should consider your different choices. You should use the following points to compare the tools:

• Is it open-source software or not?
• Capacity of storage
• Has an easy-to-use interface or not
• Support the most popular clouds (like AWS) and storage types or not
• Cost

Conclusion

An effective data versioning system integration enables companies to deploy models to production with a developed data context. The data versioning solution aids the practice managers in maintaining regulatory compliances, which is essential for any MLOps practice, while also enabling flexibility and traceability in the design.