As companies seek to deploy AI at scale, a combination of technology, talent, and techniques is required to build an end-to-end data pipeline that can produce impactful and accurate ML applications.
In a special guest feature with CXO Outlook, Sudeep George, VP of Engineering at iMerit outlines the key factors when scaling AI.
Here are 5 key takeaways from the article:
- Data quality: The quality of the data required for an application directly impacts performance, which is amplified while considering AI at scale. Enterprise-grade AI projects often work with millions of data points, potentially of different formats and from different sources. Each and every data point has to be annotated with specific instructions and to serve different functions. When the POC and pilot stage are crossed and the number of workflows and experts working on the data increases, consistency becomes mission-critical.
- Expertise and skill levels: An AI project requires experts from different disciplines and the composition and caliber of the team are significant factors when it comes time to scale. The types of expertise that help augment an AI process can be broadly divided into technical expertise, domain expertise, and process expertise.
- Edge case management: Edge cases or rare occurrences in data are typically seen within the last mile of the AI development lifecycle. These are caused by the complexity and sheer variations that exist in the real world and need to be represented in the data, to train the AI model to recognize and react appropriately.
- Data security and governance: An investment in AI fundamentally involves an investment in robust information security infrastructure. In a world where remote work is increasingly prevalent, there is also greater scrutiny to ensure that the control of on-premises security is also applied when an employee is working elsewhere.
- Continuous training: The process of AI development is a continuous one – as the world around us evolves, AI products must adapt accordingly. The data that is being collected and used in AI is continually changing and data pipelines must be built with this in mind.
Read the complete article here: Building a Data Pipeline For Scalable AI