Dataset Versions Management in ML Projects

When we look into the newer practices of building machine learning projects, we find the involvement of well-designed and sustainable systems and applications that leverage ML models and other techniques connected with a data system. However, since data works as a field for these systems, we also need to ensure that data flow needs to be highly optimized, seamless and accurate from data entry points to results from outcome points. A single wrong coming in this flow can have harmful impacts on the ML project and workflow.

In the life span of such applications and systems, algorithms and models are required to keep updated constantly, redeployed and maintained regularly, which also represents the need for proper data-version management. Just for the sake of information, let’s consider an example of fraud detection systems that now a day enables ML algorithm within and recognizes data patterns. Based on that, it defines the reliability of the customer. Keeping such a system updated with the evolving domain requires constant updating. In such domain, we frequently find large data volumes over time, complex algorithms and increasing compute resources. These are signs of a scalable ML project environment.

Such an environment also represents many changes on the incoming data side. Technical requirements and industry shifts can be the reason behind the changes in the data. Making ML projects robust and flawless against these changes requires appropriate tracking and maintaining the data version. This tracking and maintaining the versions of the dataset can be called dataset version management. However, dataset version management is a crucial task for maturing ML projects, and during the process, any ML team can face the following pain:

Large data volume management without opting for data management platforms.
Ensuring the quality of datasets.
Incorporating multiple additional data sources.
Time-consuming data labelling process.
In case of sensitive data, ensure robust security and privacy policies.

In case of failure, these pain points can lead to various issues such as model degradation, data drift, security issues and many more. So as we move forward into the article, we will see in the space of data version management, associated challenges and other necessary things to adopt during ML project development.

Let’s start with knowing what data version management is,

What is Dataset Version Management?

Dataset Version Management is the process of tracking and managing different versions of a dataset. It involves creating and maintaining a history of changes made to the dataset over time, including modifications, updates, and additions. The goal of Dataset Version Management is to provide a systematic and organized way of managing datasets, allowing users to access and use different versions of the same dataset for different purposes. It is particularly important in collaborative and iterative projects, including long-term ML projects, where multiple individuals or teams are working on the same dataset and need to ensure that they are using the correct and most up-to-date version.

In the case of machine learning projects, dataset version management helps to maintain the integrity and consistency of the data throughout the project’s lifecycle. By tracking changes to the dataset, data version management enables reproducibility and transparency, ensuring that the results can be replicated and validated.

In addition, data version management helps to avoid errors and inconsistencies that can arise when different team members work with different versions of the same dataset. It also facilitates collaboration among team members by providing a centralized repository for the data, where everyone can access the latest version.

Challenges Associated with Dataset Version Management

In real-life scenarios, we have seen that the demand for data increases with the advancement of ML projects, for example, a project designed to recommend a product from an e-commerce website to its user and requiring changes frequently due to factors such as product evolution, changing customer behaviour and many more. In addition, other additional data features likely need to be considered over time so that team can ensure data relevance and accurate results.

This frequently increasing data requirement makes us think about data management solutions, and one of the most sustainable solutions is data versioning. It is not simply applying data management tools that address all the challenges experienced by ML/AI/Data teams. Some of the commonly associated challenges are as follows:

Data Aggregation: As the demand for data increases in ML projects, the number of data sources also increases, and in such scenarios, existing data pipelines need to be capable of accommodating newer data sources. There are higher possibilities that the new dataset version contains a structure different from the older one, and keeping track of what data source contains what data structure becomes a challenge.

Data Preparation: As the structure of data evolves or changes over time, we also need to evolve and change the data processing pipelines so that the data can match the structure that an ML project needs. At times, have back compatibility with previous dataset version data structures.

Data storage: As the project progresses, issues related to the scalability of both horizontal and vertical data storage become increasingly important to address, especially as the demand for storage capacity grows over time. Proper management and planning for data storage are essential to ensure that the project can handle the storage requirements throughout its lifespan.

Data Extraction: When working on machine learning projects with multiple dataset versions, it becomes crucial to keep track of which version corresponds to particular model performance. This makes it easier to retrieve the appropriate dataset version when needed and helps in reproducing the same results consistently.

The above-discussed challenges a team faces while managing the data versions in building and productizing ML projects, but the below challenges ML projects when data changes(if data pipelines are included in ML project).

Data Drift and Concept Drift

Often the ML projects that handle large data volumes evolving with time and handling complex data may face two major challenges: concept drift and data drift.

Talking about concept drift, we can say that Concept drift refers to the phenomenon in which the statistical properties of a dependent variable or the relationship between the dependent variable and its predictors change over time. In other words, it occurs when the distribution of data changes over time in a way that invalidates the assumptions made by a machine learning model. This can lead to declining performance of the model over time, as it becomes less accurate at predicting new data. It is a common problem in many real-world applications of machine learning, particularly those involving streaming data or data that changes over time. Detecting and handling concept drift is an important consideration in developing and maintaining machine learning models.

Data drift is a concept where the statistical properties of the data being used for machine learning model training and prediction change over time in unexpected ways. This can lead to performance degradation in model performance, as the model is no longer able to generalize to new data that differs from the original training set. Data drift can be caused by various factors, such as changes in the data source, data collection processes, or changes in user behaviour. Monitoring for data drift and updating machine learning models accordingly is an important part of maintaining model performance over time.

Let’s just understand this using only one example where an e-commerce website that uses machine learning to recommend multiple products to its users based on their browsing behaviour and purchasing history.

Concept drift in this scenario could occur if the website adds a new category of products, such as home appliances, to its inventory. The existing machine learning model may not have been trained on this new category of products, resulting in a change in the distribution of the input data and potentially leading to a decrease in the model’s accuracy.

Data drift, on the other hand, could occur if the behaviour of the website users changes over time. For instance, during the holiday season, users may be more inclined to buy gifts for others, providing a shift in the distribution of the input data. If the machine learning model is not updated to account for this shift, it may lead to inaccurate recommendations being made to the users.

Data annotation and preprocessing

Large-scale machine learning projects necessitate the management of significant amounts of data. However, manually annotating data may become laborious and result in discrepancies, making it difficult to maintain the quality and consistency of annotations.

Data preprocessing is the process of transforming completely raw data into the most suitable format for analysis and modelling. As long-term projects advance, the preprocessing methods may evolve to better tackle the problem at hand and enhance model performance. This could result in the developing of numerous dataset versions, each with its own set of preprocessing techniques. Other challenges related to data annotation and processing are as follows:

Complex data growth often leads to time-consuming and inefficient processing tasks, which also affect training time and decrease efficiency.
The lack of standardization in processing the data leads to unstable quality and accuracy of the data.
Maintaining the quality and consistency of data is also challenging, often more challenging when the raw data changes.

Data security and privacy

Maintaining data security and privacy in ML projects is essential, and applying data versioning management with this becomes a challenging task to complete. Where data security ensures data protection from unauthorized access, destruction and modification during data storage, processing and transmission. On the other hand, data privacy ensures the protection of personal information and gives the right to handle and control information sharing of every individual.

In machine learning projects, managing and versioning datasets is essential, but it also poses significant challenges in terms of data security and privacy. Protecting sensitive data within machine learning pipelines is crucial to prevent severe consequences such as legal liability, loss of trust, and reputational damage caused by security breaches or unauthorized data access. Therefore, implementing robust data security measures is essential to ensure the privacy and confidentiality of sensitive data throughout the machine learning project’s lifecycle.