Strategies to Align AI Data Collection and Management with DevOps Practices

Strategies to Align AI Data Collection and Management with DevOps Practices

DevOps is characterized by the acceleration of processes to ensure continuous delivery without compromising high software quality. Balancing speed and quality is quite a challenging task, though. Data issues are among the most significant problems encountered by DevOps teams. These can be worse in the context of AI development, where massive amounts of data play a crucial role in machine learning.

There is a need to rethink data management in a DevOps environment. For example, according to a Dimensional Research study, nearly half of DevOps teams say that they find it extremely difficult to accelerate their database release cycles in line with the expedited pace of DevOps. To address this and other data-related issues in DevOps, it helps to adopt the following strategies.

Establishing clear data guidelines

It helps to have a straightforward and clear-cut way of managing data. Important decisions like using data warehouses or data lakes should be agreed upon promptly. Then, these decisions should be communicated clearly to everyone involved to ensure consistency and faster access to data whenever needed. Clear data guidelines support effective collaboration and reduce the time it takes to gather, organize, and use data. Teams avoid redoing data-related tasks as they eliminate misunderstandings or conflicts in data handling.

Ensuring data quality and structure

With DevOps going full throttle to attain speedy development cycles and product releases, there is virtually no room for changes or corrections because of inaccuracies, inadequacies, and other data quality issues. Spending so much time eliminating redundancies, verifying or correcting inaccurate data, and checking imbalances in data representation does not coincide with DevOps principles. As such, an AI development team’s data management platform should ensure that data quality is at its optimum right from the start.

On the other hand, it is vital to organize unstructured data to make it usable for machine learning. Datasets fed to AI systems are not simply collected and sent for processing in raw form. It needs to be in a format that works with the AI system being developed. Forcing unstructured or only partly organized data into the system is a fatal mistake in AI development. It only results in a dysfunctional or futile product.

Employing self-service business intelligence

For organizations collecting data from customers or users, it is advisable to use a self-service business intelligence (SSBI) solution. Using SSBI enables the creation, evolution, operation, and scaling of business data warehouses even without proficiency in business intelligence or related functions like analytics, statistical processing, and data mining. Important decisions like using data warehouses, data composability tools like The Codex or data lakes should be agreed upon promptly.

SSBI makes it possible to compile valuable data that is near-ready for direct machine learning. This significantly accelerates the data collection and preparation process, which aligns with the requirements of DevOps practices. SSBI does not apply to all AI use cases, though.

Data management is already challenging. Cleaning and preparing data for machine learning is even more resource-intensive and meticulous. However, organizations can speed up data management processes with the right strategies. These development speed-bumping strategies allow teams to proceed with AI development in a DevOps environment.