0
To successfully transform a legacy database into an AI-ready database, organizations can use automation and DevOps principles to significantly enhance efficiency, reliability, and scalability. Harness scaling, backup, recovery, monitoring and alerting, self-service database provisioning, and policy as code to ensure seamless management and deployment of AI-ready database architectures. Organizations can also incorporate infrastructure as code, configuration management, automated provisioning, and continuous integration/continuous delivery (CI/CD) for database schema and configuration changes.
The quality of the personnel who will build and implement an AI-ready database architecture is essential to the initiative’s success. Effective and ethical AI/ML implementation within database architectures is a shared responsibility that spans various organizational roles. AI/ML engineers and data scientists, data engineers, database administrators, AI architects, data governance and compliance teams, and business leaders and stakeholders all hold varying degrees of responsibility. That makes it vital for organizations to employ the right talent.
They can accomplish this by clearly defining roles and responsibilities, integrating ethics and governance into hiring criteria, providing specialized training and upskilling, fostering cross-functional collaboration, establishing centers of excellence or working groups, promoting a culture of continuous learning, seeking external expertise, implementing robust review processes, and investing in tools and technologies. Organizations that take the time to assemble a talented, competent team and conduct thorough analysis and planning before implementation can efficiently turn their legacy database into a modern architecture that maximizes performance flexibility, reduces model training time, and enables faster deployment of highly effective AI-driven solutions. These benefits can power companies to new heights in the future.
Vignyanand (Viggy) Penumatcha is a cloud database modernization expert with more than 15 years of experience in database architectures. For the last eight years, he has specialized in cloud migrations for the healthcare, insurance, telecom, education, and financial industries. His expertise spans DevOps, infrastructure as code, automation, and delivering scalable, secure, and efficient solutions across relational and NoSQL databases. He holds a master’s degree in engineering and technology management from George Washington University. He is also a Senior Member at IEEE and a Fellow Member at Soft Computing Research Society. Connect with Viggy on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE's position nor that of the Computer Society nor its Leadership.