Conference Description
Key Takeaways
- Combined conference bringing together Kubernetes, OpenStack, and PyTorch communities in a single venue
- Focus on the convergence of cloud native infrastructure and artificial intelligence workloads
- Designed for developers, architects, DevOps professionals, and technical decision-makers
- Two-day programme featuring keynotes, technical breakout sessions, and solution showcases
- Strategic sponsorship from Huawei with platinum participation from Cambricon and Intel
Introduction
KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China 2026 represents a significant consolidation of the open source infrastructure and machine learning communities in the Asia-Pacific region. Scheduled to take place in Shanghai, the event brings together practitioners working across Kubernetes, OpenStack, and PyTorch under one roof. This convergence reflects the growing interdependence between cloud native platforms and AI workloads, as organisations increasingly require infrastructure capable of supporting both containerised applications and computationally intensive machine learning pipelines.
About This Event
The conference spans two days and combines multiple flagship gatherings that have traditionally operated independently. By uniting KubeCon + CloudNativeCon with the OpenInfra Summit and PyTorch Conference, organisers have created a venue where cloud infrastructure specialists can engage directly with machine learning engineers and data scientists. The programme includes keynote presentations, technical breakout sessions, and solution showcases designed to facilitate knowledge transfer between these adjacent disciplines.
Co-located events and community experiences supplement the main programme, providing opportunities for deeper engagement with specific projects and working groups. The format emphasises hands-on technical content rather than high-level overviews, catering to practitioners who need actionable insights they can apply within their organisations.
Cloud Native Infrastructure Meets Machine Learning
The decision to combine these conferences acknowledges a fundamental shift in how organisations deploy and operate AI systems. Kubernetes has evolved from a container orchestration platform into the de facto standard for managing distributed workloads, including machine learning training and inference. PyTorch, meanwhile, has become one of the most widely adopted frameworks for developing neural networks and deep learning models. The intersection of these technologies presents both opportunities and challenges that the conference aims to address.
OpenStack continues to play a significant role in private cloud deployments, particularly among enterprises and research institutions that require control over their infrastructure. The inclusion of the OpenInfra Summit ensures that organisations operating hybrid environments or maintaining on-premises infrastructure can participate in discussions about integrating these systems with cloud native tooling and AI platforms.
Industry Participation and Sponsorship
The sponsor roster reflects the strategic importance of the Chinese market for cloud native and AI technologies. Huawei serves as the strategic sponsor, with Cambricon and Intel at the platinum level. Gold sponsors include Alibaba Cloud, ClickHouse, FlagOS, and LFOSSA, while Ant Group, ByteDance, Daocloud, and SUSE participate at the silver tier. The SODA Foundation represents the non-profit sector, and numerous community partners including COPU, Kaiyuanshe, OpenAnolis, and OpenEuler contribute to the event’s technical programme.
This mix of hyperscale cloud providers, semiconductor companies, database vendors, and open source foundations illustrates the breadth of the ecosystem now engaged with cloud native AI infrastructure.
Who Should Attend
The conference targets technical professionals responsible for building, deploying, and operating cloud infrastructure and machine learning systems. This includes software engineers, infrastructure architects, DevOps practitioners, site reliability engineers, and data scientists. Technical decision-makers such as CTOs and engineering directors will find value in sessions addressing architectural patterns and technology selection. The event also serves open source contributors seeking to collaborate with project maintainers and community members from across the region.
Organisations grappling with the operational complexity of running AI workloads at scale, or those evaluating how to modernise existing infrastructure to support machine learning initiatives, will find relevant content throughout the programme.