InfoQ Launches Three AI and Engineering Team Certification Cohorts in November

Technology media and community platform InfoQ will launch three online certified cohorts (collective learning programs) in November 2024. The subject areas cover a wide range of topics including building AI systems in production environments, search and agent design, engineering team development workflows, definition of AI-related roles and responsibilities, and performance measurement methodologies.

As more organizations transition from the stage of "experimenting with AI" to "operating AI as actual services," demand is growing for production-grade AI knowledge and team design expertise. Behind this trend lies the fact that issues around reliability, scalability, and team structure, which are less visible in the prototype phase, tend to emerge clearly in actual production environments. InfoQ's program this time can be positioned as designed to address these practical needs.

The three cohorts will be facilitated by Adi Polak, Lan Chu, and Olimpiu Pop. The covered content comprises practical, implementation-focused topics including production AI system design and operations, design methodologies for RAG (Retrieval-Augmented Generation) and AI agents, improvement of engineering team development workflows, role division in AI projects, and approaches to effectiveness measurement.

RAG refers to a methodology that combines company data and up-to-date information with large language models to improve response accuracy, and is becoming a widely adopted technology in corporate AI adoption. AI agents, meanwhile, refer to AI systems that autonomously execute multiple tasks and are gaining attention in the context of business automation. The fact that this cohort addresses these topics reflects the skill sets demanded in the field.

InfoQ is known as a media platform providing technical information to software developers, architects, and team leaders, and its online cohort-based certified programs are positioned as part of its learning services. The cohort format refers to a style where participants learning the same content during the same period form a group and advance their learning together. Unlike self-paced video learning, it is characterized by the ability to deepen knowledge through discussion with peers within a defined timeframe.

With the rapid proliferation of AI technology, "how to learn" has become a critical issue for engineers and team leaders. A comprehensive perspective encompassing not only individual technical skills but also team-wide design, role clarification, and measurement standard establishment is now required. Practical, implementation-oriented learning opportunities like this program can be understood as responding to such demand. Going forward, how such practical AI talent development programs are organized and expanded will continue to merit attention as a factor influencing the quality of corporate AI adoption.