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Advancing Excellence in Computing and Technology for 80 Years
The IEEE Computer Society is the world’s leading organization for computing professionals. As we celebrate our 80th anniversary in 2026, we continue to build on a rich legacy through conferences, publications, and programs that bring together computer science and engineering leaders at every stage of their careers. Click here to learn more and celebrate community.
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Announcements
Scaling Relational Databases in the Era of Artificial Intelligence—Q&A With Vinodkrishna Gopalan
Relational databases are undergoing a profound transformation. Artificial intelligence (AI) and machine learning workloads exhibit access patterns that diverge sharply from traditional online transaction processing (OLTP) approaches. Large scans, unpredictable queries, vector search, low-latency inference, and distributed applications are redefining how teams address schema design, indexing, scaling, and operational strategy.Vinodkrishna Gopalan is a subject-matter expert with more than 20 years of experience building and scaling enterprise software, with a strong focus on database architecture and performance. At Amazon Web Services (AWS), he led engineering initiatives for relational databases, application networking, and middleware powering millions of applications. Drawing on his expertise in distributed systems, cloud-native architecture, and AI-driven data access, Gopalan offers insights about how relational databases are evolving for AI and machine learning (ML) workloads.Q: How are AI and ML workloads changing traditional database traffic patterns and performance assumptions?Gopalan: Traditional relational databases are architected for online transaction processing (OLTP) workloads, characterized by small, fast, and frequent read-and-write operations. AI and ML workloads operate differently. Training pipelines require reading vast quantities of historical data, often terabytes, and place significant pressure on storage, network, and compute resources, far exceeding the demands typically associated with transactional workloads.Query behavior also changes. Traditional applications usually have predictable access patterns, making it easier to tune queries, configure indexes, and implement caching. ML workloads are often more exploratory and ad hoc, meaning they may not follow the patterns a typical optimizer expects.There is also a distinction between training and inference. Training workloads can tolerate slightly higher latency per operation as long as the overall process remains fast. Real-time inference, on the other hand, often requires lower latency than traditional applications. Resources can become overloaded in unanticipated ways, and standard caching methods may not work when workloads scan large amounts of data rather than repeatedly accessing the same…
Paul Chaney
How to Train and Retain Top Software Engineering Talent
According to Hiring Lab, postings for software engineering jobs have increased by 15% since 2025, and 71% of that increase is aimed at experienced engineers. That is, the very engineers that organizations rely on to oversee AI-assisted software development.In this competitive environment, organizations that fail to offer professional development and clear career pathways risk losing some of their most valuable engineering talent. Those departures can create hard-to-fill gaps that ripple across engineering teams and the broader organization.In a tight hiring market, replacing experienced software engineers becomes more difficult and more expensive.Given this, training and retaining your top talent are essential strategic investments.As LinkedIn’s Workplace Learning Report 2025 highlights, providing training and advancement opportunities are key to increasing employee engagement and retention.The report also found that Providing learning opportunities is the leading strategy organizations use to improve employee retention. Organizations with mature career development programs report greater confidence in their ability to attract and retain talent, as well as a stronger outlook on profitability. To meet today’s urgent software engineering challenges, these programs must look beyond training in specific tools and technologies and focus on enduring software engineering capability.The Software Engineering Body of Knowledge (SWEBOK) V4.0a and IEEE Computer Society Software Professional Certification Level 2 offer organizations a practical, consensus-based framework for developing and validating engineering capability across their teams.The Cost of Untrained Talent and Uncertain PathwaysThe Society for Human Resource Management’s research indicates that many organizations spend less annually on employee learning and development than the direct cost of recruiting a replacement employee. And this SHRM assessment does not account for the costs of onboarding, lost productivity, and knowledge transfer.Pluralsight’s 2025 Tech Skills Report spells out the potential costs and benefits in stark numbers: On average, upskilling existing employees costs about $5,770, compared with $14,170 to hire new technical talent.…
IEEE Computer Society Team
Episode 10 | From Student to Region 6 Director: An IEEE Journey
Navigating a career in technology requires more than just mastering technical frameworks. It demands an understanding of how interconnected global networks, professional communities, and leadership ecosystems drive large-scale impact.In Episode 10 of InfiLab, host Sowmya Chintakindi sits down with the IEEE Region 6 Director to dissect a remarkable trajectory from student member to high-level regional leadership. Together, they break down how active participation in professional organizations can accelerate career growth, build resilient networks, and empower engineers to create lasting contributions in their fields.Key Discussion Highlights: Strategic Career Escalation: How leveraging volunteer and leadership roles early in your student career builds transferable management, governance, and operational skills. Navigating Complex Organizational Structures: Insights into leading Region 6, including balancing local section needs with broader organizational goals and technology trends. Overcoming Transition Friction: Key strategies for successfully bridging the gap between academic student chapters and full-scale professional industry leadership. Building Value-Driven Professional Networks: Practical approaches to cultivating meaningful, long-term industry connections through community-driven initiatives and technical leadership. Watch the full discussion below! Expand Your Technical & Professional Horizons: Meet the Voice Behind InfiLab: Get an inside look at the podcast’s mission and learn more about host Sowmya Chintakindi’s drive to empower the tech community here. Elevate Your Professional Credentials: Gain a competitive edge in the job market by exploring the IEEE Computer Society’s Software Professional Certifications. Map Out Your Next Career Move: Unlock a curated collection of career guides and professional development resources built to help you navigate your career.
Sowmya Chintakindi
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