In today's data-driven world, the notion that critical information should be made available in real-time surpasses the simple requirement of technology; it's the very bedrock on which modern businesses are built. For Sumit Shekhar, this realization formed the cornerstone of one of his most transformative projects: migrating the very vital databases onto a public cloud infrastructure, for example, AWS (Amazon Web Service). As the Senior Product Director of AI Infrastructure Platforms at a leading financial institution, Sumit was given the challenge of a system that needs to overcome both speed and availability while showing support for the global-scale operation of the institution and AI services.

This was no ordinary migration; it was a complete makeover in the way the organization managed and processed data, so that the infrastructure could finally keep up with the demanding pace at which financial transactions travel today. Let's delve deep into how Sumit Shekhar, as a helmsman, with his innovative approach toward cloud technology, reshapes the future of AI-driven services in the world of finance.

Sumit Shekhar is a prominent expert in AI Infrastructure and cloud computing. A Senior Product Director of AI Infrastructure Platforms at a leading financial institution, he has led several transformational projects and practically reinvented how organizations would harness the power of the cloud and AI. Working on migrating critical databases to a cloud-native system set a new bar for real-time data processing and AI-driven innovation, cementing his status as a truly visionary leader in the field. With Sumit at the helm, guiding events with his razor-sharp strategic mindset and innovative use of technology, he becomes an agent of meaningful change within the world of financial services.

Moving Mountains: Migrating Critical Databases

Data migration is never easy, and when it involves very critical databases, such as SQL database, NoSQL database, and In-Memory Database (IMDB), respectively that house voluminous sensitive information belonging to one of the world's largest financial institutions scale and complexity of the project go to extreme lengths. It required not only seamless data movement but also real-time processing capability, integrity of data, and a reduction in operational cost. For illustration purpose, an example of SQL database on AWS would be Cockroach or Amazon Aurora, a NoSQL database would be Amazon DynamoDB or CassandraDB, as well as an IMDB would be Redis, Memcached, or Oracle.

For Sumit, it was not just about moving data from point A to point B; rather, he wanted to rethink the whole architecture of the data management system to align with the institution's eventual goal of leading with AI/ML-first infrastructure. Sumit wanted to build a scalable system that would handle global operations, delivering unprecedented uptimes for the mission-critical applications, by moving these databases onto AWS and building a cloud-native infrastructure.

It involved the integration of distributed databases, which are critical to ensuring data availability across multiple regions. Uptime was improved to as high as Five9s or 99.999%, thus enabling real-time transaction processing and making sure the institution never faced downtime-a key requirement in the world of finance moving at the rhythm of a nanosecond.

Beyond Data: Accelerating AI-Driven Services

The real innovation of this project was in its ability to influence the institution's AI capabilities. In an industry where decisions are increasingly based on real-time data, the speed at which that information gets processed and analyzed goes directly into the heart of AI model effectiveness. For Shekhar, this was creating more than a faster system or even a more reliable one; it was creating an enabling system for AI innovation across his organization.

Access to real-time data was faster and more efficient with the new cloud-native infrastructure. Because of this, AI model training improved by double-digit numbers than it had been before, particularly for fraud and risk management. The ability to process and analyze data in real time increased the speed of AI-driven services toward each new emerging threat, hence enhancing the general security posture of the institution.

By streamlining this data pipeline, Sumit’'s work not only enhanced the institution's AI powers but laid the ground for future innovation in machine learning and data analytics. Quicker model training times allowed teams to tinker with new AI use cases and ways to apply these technologies, opening further doors toward even more intelligent solutions in areas that included customer engagement, new personal finance products, and advanced risk management.

A Strategic Balancing Act: Leading Cross-Functional Teams

Sumit leads in a way that showcases his signature ability to orchestrate large-scale technology transformations while keeping all stakeholders in alignment. The cross-functional collaboration required at all levels, from engineering teams executing on the technical migration of critical databases to cloud through compliance officers who secured the new system was going to meet all regulatory standards.

Sumit, while being pivotal as a Senior Product Director, knew well that such a project needed not just the technological facets but the bonding of all in putting in their efforts together. His skill at maintaining open communications and keeping his eyes firmly on the strategic objectives of the project lay at the heart of the entire success.

This approach extended as well to the AI and machine learning teams of the institution. By working closely with data scientists and engineers, Sumit made sure that the new infrastructure met not only the current needs but was also flexible enough to meet the demands of future innovations. By so doing, he kept the institution very sharp with the latest in AI technologies while ensuring that all regulatory and security requirements were met.

Driving Business Value: Cost Savings and Efficiency Gains

While the technical achievements of this project are impressive, what made this project so special was that it had real business value. The ability to transfer critical databases to the cloud would allow the institution to save an estimated 15% yearly in operational cost ($XXBn), saving these funds and allowing them to be reinvested into other strategic initiatives.

By optimizing the cloud-native processes, Sumit and the engineering team were able to achieve better performance without losing out on reliability or security. Further, the distributed architecture of these databases (both SQL and NoSQL) allowed for more resiliency since one would not feel the unavailability of data if any part of the system went down. Meanwhile, Oracle database migrations were seamless: there was no loss of data, and it did not affect day-to-day operations-evidence enough of Shekhar's meticulous planning and smooth execution.

But perhaps even more relevant, this focus on efficiency and cost reduction was with the AI-driven services of the institution. Improving speed and reliability down the data pipeline, Shekhar had achieved decision-making at a far quicker rate, with more granular predictions than ever before, hence making the institution substantially more responsive to market changes and emerging risks in near real time.

The Future of Data in Financial Services

The critical work that Sumit Shekhar has performed in migrating critical databases to a cloud-native infrastructure represents a leap forward in the evolution of financial technology. Shekhar designed a system that powers real-time data processing and AI-driven services. He significantly upgraded the current capability of the institution for the future.

The world of finance is changing fast, and the manner in which data will be managed and processed will define success for most of them. With his work on this project, Shekhar has proved that cloud-native infrastructures are not a stepping stone to cost-cutting but form the bedrock on which AI in financial services will be based.