This article is based on coverage originally published by GeekWire in May 2019, previewing speakers at the GeekWire Cloud Summit.
Why DevOps Matters More Than Ever in the Cloud Era
As software-as-a-service (SaaS) became the dominant delivery model for enterprise computing, the pressure on development teams to ship faster and more reliably grew dramatically. DevOps—the cultural and technical practice of unifying software development and IT operations—emerged as one of the most effective strategies for meeting that demand.
Rather than treating development, QA, and operations as separate silos, DevOps organizations use automation, continuous integration and delivery (CI/CD), infrastructure-as-code, and shared accountability to shorten release cycles, reduce failures, and recover faster when things go wrong. For companies migrating to or building natively on the cloud, these practices are no longer optional—they are foundational.
Core DevOps Practices Driving Modern Software Teams
To understand the breadth of DevOps today, it helps to look at the specific practices and disciplines that define it in production environments:
- Continuous Integration (CI): Developers commit code to a shared repository multiple times per day. Automated builds and tests run on every commit, catching integration issues early.
- Continuous Delivery/Deployment (CD): Code that passes automated testing is automatically prepared—or even deployed—to production, reducing manual handoffs and release risk.
- Infrastructure as Code (IaC): Server provisioning and configuration are defined in version-controlled code, making infrastructure reproducible and auditable.
- Observability and Monitoring: Teams instrument applications and infrastructure to collect metrics, logs, and traces, enabling faster incident detection and root-cause analysis.
- Open Source Tooling: Much of the modern DevOps toolchain—Kubernetes, Prometheus, Terraform, Chef, and others—is open source, lowering barriers to adoption and encouraging community innovation.
Five Practitioners Shaping Modern DevOps
The following five professionals represent a cross-section of DevOps expertise—from platform engineering and open source tooling to cloud-native infrastructure and observability. Their backgrounds illustrate how DevOps spans roles, industries, and technology stacks.
Tara Hernandez — Engineering Manager, Google Cloud
Hernandez is recognized as an early advocate of continuous integration. CI is a discipline that requires developers to merge their changes into a shared codebase frequently—often multiple times per day—with automated validation at every step. At scale, this practice dramatically reduces the cost and complexity of integrating large teams’ work, and it forms the backbone of modern CI/CD pipelines.
Nell Shamrell-Harrington — Chef (now Progress Chef)
Shamrell-Harrington works at Chef, one of the pioneering companies in infrastructure automation and configuration management. A significant milestone in Chef’s history was its decision to make all of its core product code open source—a strategic move that reflects a broader industry trend toward open, community-driven tooling. Open-sourcing infrastructure tools lowers vendor lock-in risk and allows practitioners to contribute fixes and features directly.
Illya Chekrygin — Founding Engineer, Upbound
Chekrygin brings hands-on cloud migration experience from a high-profile project at HBO, where he was involved in moving the company’s streaming service to Amazon Web Services using Kubernetes as the container orchestration layer. This type of large-scale migration—taking a latency-sensitive, high-traffic consumer application and re-platforming it onto managed cloud infrastructure—is one of the most complex challenges in modern DevOps. He later co-founded Upbound, a Seattle-based startup focused on cloud-native infrastructure tooling.
Lee Atchison — New Relic
Atchison has spent years working on cloud strategy at New Relic, a company that provides application performance monitoring (APM) and observability tooling. Observability—the ability to understand a system’s internal state from its external outputs—is a cornerstone of mature DevOps practice. Teams that can measure what their systems are doing in production can iterate faster, respond to incidents more confidently, and make data-driven architectural decisions.
Idit Levine — Co-founder and CEO, Solo.io
Levine focuses on helping organizations adopt cloud-native technologies such as Kubernetes and serverless computing. Solo.io builds tooling around service mesh and API gateway patterns—infrastructure that manages how microservices communicate securely and reliably. As organizations decompose monolithic applications into distributed services, this layer of the stack becomes increasingly important for maintaining reliability, security, and observability across complex environments.
Key Takeaways for DevOps Practitioners
Looking across these five practitioners, several themes emerge that are relevant to any engineering team investing in DevOps today:
- CI/CD is table stakes. Continuous integration and delivery are no longer differentiators—they are the baseline expectation for competitive engineering teams.
- Open source tooling dominates. The most widely adopted DevOps tools are open source. Understanding contribution models and community health is part of evaluating tooling choices.
- Cloud-native migrations require operational discipline. Moving to Kubernetes and managed cloud services demands deep understanding of networking, storage, and reliability engineering—not just containerization.
- Observability is a force multiplier. Teams that instrument their systems well move faster and recover from incidents more efficiently than those operating blind.
- Service mesh and API management are maturing fast. As microservices architectures become common, the infrastructure connecting those services—including service meshes—is an active area of tooling development.
The Broader DevOps Landscape
DevOps is not a product you purchase or a certification you earn—it is an ongoing organizational and technical practice. The practitioners highlighted here represent different entry points: tooling, platform engineering, observability, cloud migration, and emerging infrastructure patterns. What they share is a focus on reducing friction between the act of writing software and delivering reliable value to users.
For engineering leaders evaluating where to invest, the message is consistent: automation, observability, open standards, and cultural alignment between development and operations teams remain the highest-leverage areas for improving software delivery performance.
Original reporting by GeekWire. Speaker details reflect information available at the time of the 2019 GeekWire Cloud Summit announcement.