Building Strong Data Pipelines Crucial to AI Training

Data pipelines are essential for training AI models because they help manage the flow of data from its source to the point of analysis.

These pipelines simplify managing and analyzing data, making it easier to train effective and explainable AI models. Data pipelines also enable organizations to scale their AI efforts by streamlining the collection and processing of data from various sources.

This can reduce the time and resources required to prepare data for machine learning, allowing organizations to train models faster and more efficiently.

Understanding the data’s source and underlying sensitivity also helps identify security and privacy implications of AI models.

AI Training and Data Requirements

Bob Friday, chief AI officer at Juniper Networks, explains training AI requires vast amounts of data, and that data must be preprocessed and transformed into a format that can be fed into machine learning algorithms.

“Data pipelines enable data engineers to automate these processes and ensure that data is consistently and accurately prepared for use in machine learning models,” he says.

Additionally, data pipelines can help address data quality issues, such as missing data or inconsistent data formatting.

“By automating data quality checks and data cleaning, data pipelines can ensure that machine learning models are trained on high-quality, accurate data,” Friday says.

Sean Hughes, AI ecosystem director for ServiceNow, explains for AI to provide human decision support -- for example, in the form of natural language text summarization, categorization, classification, or even prediction -- it first needs to be trained on the tasks.

Training can enable general knowledge for AI powered search across a customer-facing knowledge base, or it could be used to focus on more specialized subject matter areas unique to a business, including risk assessments for loan application processing.

“The data pipeline automates the collection, processing, and transformation of data that the AI will learn from to complete the task with the level of knowledge needed for the user to be satisfied with the response,” he says. “Without a robust data pipeline, results will vary.”

That means the source data’s quality, including relevancy and accuracy, is a business imperative when training enterprise AI. Low quality data can lead to AI generated output that cannot be trusted.

Careful Planning of Pipelines

Mikhail Kazdagli, head of AI at Symmetry Systems, explains building a data pipeline requires careful planning and consideration of the problem and data sources, including sensitivity of the data being used, processing requirements, tools, and technologies.

“It's crucial to ensure data quality and security are maintained throughout the pipeline, and that it's continuously monitored and improved over time,” he says.

Friday adds when building a data pipeline, it’s important to consider key use cases and potential roadblocks and taking the step to define purpose is critical to understanding what data will need to flow through it.

“All good AI or machine learning projects should start with understanding what questions or human behavior you are trying to automate,” he explains. “Data pipeline design should also be flexible enough to accommodate changes in data sources, processing requirements, and output formats.”

A Broad Team of Specialists Needed

Kazdagli says the key stakeholders for building a data pipeline project may vary depending on the organization's structure, the project goals and the sensitivity of the data being used.

"Typical stakeholders are data scientists and data engineers, IT infrastructure team, project managers, and business analysts," he says. "Given the use of the organization’s data, cybersecurity should also be a key stakeholder."

Overall, it's essential to have a team with diverse skills and expertise to ensure the project's success.

"A poorly designed data pipeline can lead to significant security and quality issues and consequences, reducing the effectiveness of data analytics and decision-making," Kazdagli cautions. "It's essential to carefully plan and design the pipeline to ensure it meets the organization's goals and requirements."

Kazdagli points out organizations must augment their IT staff with diverse skill sets and insight into the data inventory and data flows within their organization for a successful data pipeline architecture.

"A successful data pipeline project will require not only data scientists, data engineers, but also DevOps engineers, security experts, business analysts, and project managers engagement," he says.

By having a team with these skill sets and understanding of the organization’s data inventory, organizations can ensure the data pipeline is designed, built, and maintained effectively and efficiently, and doesn’t introduce additional cyber and privacy risk.

From ML Engineers to Cloud Computing Specialists

Friday agrees organizations should be thinking about the role their entire IT staff plays in the architecture and maintenance of data pipelines.

“Data engineers are essential for designing and building data pipelines, and they need to be able to manage data integration, data warehousing, and ETL (Extract, Transform, Load) processes,” he says.

Machine learning engineers are necessary for building and optimizing machine learning models used in data pipelines, while DevOps engineers are responsible for deploying, testing, and maintenance of data pipelines.

Friday adds cloud computing expertise is essential for designing and deploying data pipelines in cloud environments, which are necessary for modern data pipelines at scale.

“Security is also a top concern,” he notes. “A poorly designed pipeline may expose sensitive data to unauthorized access, leading to data leakage and security breaches.”

More fundamentally, Friday points out poorly designed pipelines often simply operate inefficiently, making management, maintenance and scaling difficult over time, requiring increased costs, both in terms of money and time, compared to well thought-out pipelines.

“The biggest danger with a poorly designed pipeline is the risk of incorrect or incomplete data,” he cautions. “A pipeline that hasn’t been thought out may not capture all the necessary data required to train machine learning models or produce accurate insights.”

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