I’m excited to join the University of Minnesota today for the 3rd Annual ITI Technology Symposium and Advisory Board Meeting! Hosted by the Information Technology Infrastructure (ITI) program in the College of Continuing and Professional Studies (CCAPS), this year’s theme is “How to AI-Proof Your Career: Everything You Need to Know About Agentic AI.”
I’ll be participating in the “Six Subplans, One Future” industry panel, representing DevSecOps & IT Project Management in my role as an engineering manager at Jamf. I’m looking forward to joining fellow technology leaders to discuss what AI means for our work, our skills, and the next generation of IT careers.
This topic connects closely with my work across cloud platforms and AI engineering. For me, the career conversation centers on how we keep learning, apply new tools thoughtfully, and bring sound judgment to the work. As we explore the possibilities of agentic AI, questions around security, governance, reliability, and delivering value deserve a place in the discussion.
The evening brings together students, faculty, alumni, advisory board members, and industry leaders. Highlights include:
Keynote fireside chat: Sean Gayle, Director of AI Application Engineering at Microsoft, with facilitator Neely Tamminga, CEO of DISTILL.
Student Innovation Spotlight: Projects from the UMN-AWS Student Builder Group, led by captain Alex Brower.
“Six Subplans, One Future” industry panel: A discussion across six IT disciplines, facilitated by Anna Malaika Muhovich of UnitedHealth Group.
Interactive roundtables: Small-group conversations about career growth, emerging technology, and industry trends.
I’m especially looking forward to connecting with students and hearing their questions and perspectives. Events like this create an opportunity to share practical experience while learning from people approaching technology from different disciplines and stages of their careers.
Event details
Date: Tuesday, October 6, 2026
Time: 5–8:45 p.m. Central
Location: Mississippi Room, Coffman Memorial Union, Third Floor
Address: 300 Washington Avenue SE, Minneapolis, MN 55455
I’m excited to be speaking at The Cloud & AI Summit 2026, happening September 30 through October 2 in St. Charles, Missouri.
This year, I’ll be delivering two sessions covering topics I spend a lot of time working with in the real world: multi-cloud architecture and operations, and container security with Docker Scout, Azure Container Registry, and AKS.
What I like about both sessions is that they go beyond the marketing and focus on what actually happens when you have to build, operate, secure, and support these technologies in production.
We hear the term multi-cloud constantly. Organizations want flexibility. They want to avoid lock-in. They want applications that can move anywhere.
That sounds great.
The reality gets complicated very quickly.
Once you start operating across Azure, AWS, and Google Cloud, you have to deal with different approaches to networking, identity, infrastructure as code, observability, Kubernetes, application deployment, security, and more.
The bigger challenge often isn’t even the technology. It’s figuring out how your teams and operating model need to change to support multiple clouds effectively.
In this session, I’m going to dig into what can realistically be standardized across cloud platforms and where trying to force everything into a common model can actually create more complexity.
We’ll talk about areas including infrastructure as code, Kubernetes, networking, identity and access, application deployment, observability, and the structure of the teams responsible for running these environments.
The goal isn’t to convince everyone that they need three clouds.
It’s to help teams make better architectural decisions when multi-cloud becomes part of their reality.
Having worked with Azure, AWS, and GCP environments throughout my career, I’ve seen both the benefits and the operational challenges that come with multi-cloud. I’m looking forward to sharing some of those lessons.
Containers make it incredibly easy to package and ship software, but getting an image into a registry is only part of the story.
The question becomes:
What is actually inside that image?
A container can build successfully, push to Azure Container Registry, and deploy into AKS while still containing vulnerable operating system packages, outdated dependencies, or an insecure base image.
This session will focus on how Docker Scout can help teams continuously evaluate container images stored in Azure Container Registry.
I’ll walk through a practical workflow showing an application image being built and pushed to ACR, analyzing that image with Docker Scout, identifying vulnerabilities, improving the base image, and then seeing the security posture improve.
We’ll also look at dependency drift, vulnerability prioritization, Docker Hardened Images, and where container scanning fits into the larger application delivery process.
The idea is not to bolt security onto the end of your pipeline.
It’s to make security part of the normal way your teams build and deliver containers.
Bringing Cloud Operations and Security Together
There’s actually a common theme between these two sessions.
Modern cloud environments are becoming increasingly complex.
Organizations are operating across clouds. Kubernetes continues to be a major application platform. Containers are moving rapidly through CI/CD pipelines. AI is accelerating software development even further.
The answer can’t simply be adding more tools.
We need architectures, platforms, processes, and engineering teams that reduce complexity while still giving developers the flexibility they need.
That is a big part of what I plan to explore during my sessions at the Cloud & AI Summit.
Not just what the technology can do, but how we can actually operate it successfully.
Reconnecting With the Tech Community
One of the things I’m also looking forward to most about the Cloud & AI Summit is reconnecting with people I’ve worked with and known throughout my career.
Conferences like this are about much more than the sessions. They are a chance to reconnect with former Microsoft colleagues, fellow Microsoft MVPs, Docker Captains, community leaders, and friends from across the industry.
I’m especially looking forward to catching up with my former Microsoft colleague Chris Ayers, Microsoft MVPs Donnie Taylor, John Joyner, and Kris Turner, and fellow Docker Captain Shivay Lamba.
Technology changes quickly, but the relationships and communities built around it are what make events like this special. Some of the best conversations at conferences happen outside the session rooms, comparing experiences, talking about what we’re building, and learning from one another.
It will be great to get everyone together again.
See You at the Cloud & AI Summit
I’m looking forward to getting back in front of the technical community, connecting with other speakers and attendees, and having some great conversations around cloud, containers, Kubernetes, security, and AI.
The Cloud & AI Summit has sessions covering areas including Cloud Operations, AI, DevOps, Security, Data & Analytics, Pro Code, and Low Code, so there will be a lot happening throughout the three-day event.
And if you’re running multi-cloud environments, working with AKS and containers, or trying to figure out how to secure your software supply chain without slowing down your developers, I’d especially love to connect.
As cloud-native infrastructure, Kubernetes, AI, and multicloud strategies continue to evolve, I recently had the opportunity to publish several new articles on the Pluralsight Blog focused on some of the biggest operational and architectural trends happening right now.
These articles explore the growing role of AI in Kubernetes operations, the realities of multicloud cost management, and the rise of agentic tooling for cloud platforms.
New Blog Posts on Pluralsight.com
Agentic CLI for AKS: FAQs and how to use it
In this article, I break down Microsoft’s emerging Agentic CLI for AKS experience and explain what it actually is, how it works, and where it fits into Kubernetes operations. The post explores how the tool uses AI to assist with troubleshooting and diagnostics for Azure Kubernetes Service environments while still keeping humans in control of operational decisions.
Multicloud environments can create massive flexibility, but they also create significant operational and financial complexity. In this article, I explore practical multicloud cost management strategies, tooling approaches, and methods organizations can use to improve visibility and optimize spend across AWS, Azure, and GCP.
AI agents are quickly becoming one of the most interesting emerging areas in cloud-native operations. This article explores what AI agents for Kubernetes actually are, the problems they aim to solve, and some of the current tools and approaches appearing in the ecosystem. I also discuss where these systems may realistically help platform teams and where caution is still needed.
These topics sit at the intersection of AI, cloud engineering, Kubernetes operations, platform engineering, and FinOps, and they represent some of the biggest conversations happening across the industry right now. If you’re working in cloud-native infrastructure, platform engineering, DevOps, or AI-enabled operations, I hope these articles provide useful insight and practical perspective.
Be sure to follow my profile on Pluralsight so you will be notified as I release new courses!
Here is the link to my Pluralsight profile to follow me:
Ready to Level Up Your GCP Skills? My New Terraform Course Is Live. I’m excited to share that my latest Pluralsight course, Google Cloud Environments: Applying Terraform, is now live!
Infrastructure as Code (IaC) has become a foundational skill for modern cloud engineering teams, and Terraform continues to be one of the most widely adopted tools for managing cloud infrastructure consistently and at scale. In this course, I focus on how to apply Terraform effectively within Google Cloud environments to help engineers move beyond basic concepts and into practical, real-world implementation patterns.
Whether you’re a cloud engineer, DevOps engineer, platform engineer, or someone expanding into Google Cloud, this course is designed to help you build confidence working with Terraform in GCP environments.
What You’ll Learn
In this course, we walk through how Terraform can be used to deploy and manage infrastructure in Google Cloud using Infrastructure as Code principles. Topics include:
Understanding Terraform workflows in Google Cloud
Configuring providers and authentication
Managing infrastructure declaratively
Working with state management
Deploying and updating cloud resources
Using reusable Terraform configurations and modules
Applying Terraform concepts to real Google Cloud scenarios
The course is designed to help bridge the gap between learning Terraform syntax and actually applying it in cloud engineering environments.
Why Terraform Matters
Terraform enables teams to define infrastructure in code, making deployments more repeatable, scalable, and reliable. Instead of manually configuring cloud resources through portals and scripts, teams can standardize infrastructure deployments and treat infrastructure similarly to application code.
As organizations continue adopting cloud-native and multi-cloud strategies, Infrastructure as Code skills are becoming increasingly valuable across engineering and operations teams.
Hands-On Cloud Engineering Skills
One thing I always try to emphasize in technical training is practical application. The goal is not just to understand Terraform conceptually, but to understand how engineers actually use it in day-to-day cloud operations and platform engineering work.
Google Cloud and Terraform together create a powerful combination for:
Automated infrastructure deployments
Consistent environment management
Scalable cloud operations
DevOps and platform engineering workflows
Repeatable infrastructure provisioning
Pluralsight also provides broader Terraform and cloud engineering learning paths that complement these skills with additional hands-on labs and cloud-focused training.
Who This Course Is For
This course is a great fit for:
Cloud engineers
DevOps engineers
Platform engineers
SREs
IT professionals transitioning into cloud engineering
Anyone looking to strengthen their Terraform and Google Cloud skills
If you already have some familiarity with cloud concepts and want to deepen your Infrastructure as Code knowledge in GCP, this course should provide a solid next step.
I hope this course serves as a valuable resource in your IaC journey. Thank you for your continued support, and Be sure to follow my profile on Pluralsight so you will be notified as I release new courses!
Here is the link to my Pluralsight profile to follow me:
It’s hard to believe, but it’s been a couple of years since I last sat down with Richard Campbell on RunAs Radio. Technology moves fast, but the cloud landscape has matured in ways that were only just beginning during my last visit.
I recently joined Richard for my third appearance on the show (Episode #1025) to talk about a challenge that is becoming the “new normal” for major SaaS providers: Expanding a Cloud-Native stack across multiple clouds.
From Single-Cloud Roots to Multi-Cloud Reality
At Jamf, we’ve built a powerful reputation for managing Apple devices at scale. Historically, our SaaS product was rooted in AWS. However, as our customer base grows, now serving over 70k+ customers worldwide the demand for flexibility grows with it.
In this episode, we discuss the journey of bringing those SaaS workloads to Azure and AKS. It isn’t just about “moving” code; it’s about architecting for consistency without losing the unique benefits of each cloud provider.
Kubernetes: The Common Ground (But Not the Whole Story)
One of the key takeaways from our chat is that while Kubernetes (AKS, EKS, GKE) provides the common operating system for the modern cloud, it isn’t a “magic wand” for multi-cloud.
To achieve true consistency, you have to look past the orchestrator and focus on the surrounding ecosystem. We dove into the complexities of:
IaC & Deployment: Why tools like OpenTofu are becoming essential for maintaining cloud-agnostic deployments.
Observability: Using Prometheus and Grafana to ensure that your SRE teams see the same data regardless of whether the backend is Azure or AWS.
Identity: Navigating the friction between different identity providers to ensure a seamless experience for the end user and how platforms like Okta support this.
The Docker & AI Connection
We couldn’t have a conversation in 2026 without touching on the elephant in the room: AI. As a Microsoft MVP focused on AKS and a Docker Captain, I’ve been watching closely how the Kubernetes and container ecosystem is evolving to support AI/ML workloads. Richard and I spent some time discussing how Docker, Inc. is positioning itself in this space and how developers can leverage these tools to build AI-ready applications without getting locked into a single vendor’s proprietary stack.
Reflections on a Maturing Landscape
Coming back to RunAs Radio for a third time allowed me to reflect on just how much our industry has shifted. We’ve moved past the “is the cloud safe?” phase and into the “how do we optimize for a multi-cloud world?” phase.
Whether you are a platform engineer, a developer, or a technical leader, the lessons I’ve learned at Accenture, Microsoft, helping startups, and now at Jamf while scaling across multple clouds are applicable to almost any modern enterprise.
I am excited to share that I will be speaking at this year’s Open Source North conference on May 29, 2025, at the University of St. Thomas in St. Paul.
This year, I’m teaming up with my fellow Jamf, Levi McCormick (Director of Engineering at Jamf), for a session that is very close to our daily reality: Multi-Cloud Without the Marketing or Designing for Multi-Cloud Without Losing Your Mind.
Why this talk? In the cloud industry, “Multi-Cloud”, “Cloud Native”, and “Iac via Terraform” are often sold as magic pills for redundancy, cost savings, unifaction and more across clouds. But for the people actually building and maintaining these systems, it can often feel like a recipe for complexity and technical debt.
At Jamf, Levi and I work on our infrastructure efforts across AWS, Azure, and GCP. We’ve learned—sometimes the hard way—what works, what doesn’t, and where the “hype” version of cloud differs from the “production” version. We wanted to build a session that focuses on the practical:
How to design for portability without over-engineering.
Managing identity, networking, and security across different providers.
Avoiding the “lowest common denominator” trap.
Keeping your sanity while managing three different clouds.
Open Source North is a great local event to the MN Tech scene because of the high-caliber community and the focus on real-world engineering. Whether you are a cloud veteran or just starting to look at a second provider, we’d love to see you there.
The Details:
Conference: Open Source North 2025
Date: May 29, 2025
Location: University of St. Thomas (St. Paul Campus)
As devs, platform engineers, and DevOps practitioners, we all feel it: the pace of change is relentless. New tools, new architectures, new expectations, and AI. It can be hard to separate where to invest our time from hype.
That’s exactly why I want decided to write this post about the2025 Docker State of Application Development Report from Docker.
This report is not marketing fluff. It’s based on insights from over 4,500 developers and engineering professionals and offers a grounded snapshot of how application development is actually evolving today.
Although published in 2025, this report covers long-running trends that continue to shape modern application development. Areas like containerized workflows, cloud-based development environments, AI-assisted tooling, and shared responsibility for security evolve over time rather than changing overnight.
Referencing the 2025 report ahead of the 2026 release provides valuable context. It establishes a baseline for understanding where the industry is coming from, which patterns are proving durable, and which challenges continue to persist. I’ll be looking out for the 2026 report. If you havent checked it out the 2025 report yet you should.
As a Docker Captain, I strongly encourage you to read the full report. But first, here are some of the key takeaways that stood out to me:
Remote-First Development Is Becoming the New Normal
One of the biggest shifts in 2025 is how developers are working:
64% of developers now use non-local development environments as their primary setup
Only 36% rely primarily on local machines
That’s a significant change from previous years, and it speaks to the reality that cloud-based workflows, remote dev environments, and tools that unify development environments are now mainstream. This shift isn’t just a trend — it’s redefining how teams collaborate and deliver software efficiently.
Developer Productivity Still Faces Friction Points
The report highlights that, despite improvements in tooling and culture, many teams still experience bottlenecks in everyday work:
Pull requests stuck in review
Tasks without clear estimates
Slowdowns in the “inner development loop”
Even with great culture and tooling, friction still exists, especially around planning and execution. Knowing where dev productivity stalls helps us focus improvements where they matter most.
Learning Is Shifting to Self-Guided, Online Resources
Developers are reinventing how they learn:
85% of respondents use online courses or certifications
Traditional sources like books or on-the-job training are less dominant
This highlights a bigger trend in continuous learning and self-driven skill development — especially important as the pace of change in languages, platforms, and architectures continues to accelerate.
AI Adoption Is Real, But Not Uniform
AI continues to influence how software is built, but adoption is still uneven:
Some teams are deeply integrating AI tools
Others are more cautious or selective
The report frames AI as an enabler, not a magic bullet. Developers are using AI to assist with documentation, research, and repetitive tasks, but real productivity gains depend on meaningful integration into workflows and data quality.
Security Is a True Team Effort
Security is no longer siloed:
Teams of all sizes report that developers, leads, and operations are involved in security
Only a small fraction of organizations outsource security entirely
The idea that “security is someone else’s job” is gone — fixing vulnerabilities and embedding security thinking into the development lifecycle is now a collective responsibility.
What This All Means for Developers
Taken together, these findings show a software landscape that’s:
More distributed and cloud-native
More self-taught and adaptable
More collaborative around security
Still facing persistent productivity barriers
These trends have real implications for how we build teams, invest in tooling, and think about developer experience.
Go Read the Full Report
The 2025 Docker State of Application Development Report is packed with additional insights, data, and analysis. Whether you’re a developer curious about AI adoption, a manager thinking about remote workflows, or a team lead prioritizing security practices, there’s something in this report for you.
This is my first blog of the new year (2026)! Since being re-awarded as a Microsoft MVP, Microsoft provided me with a fresh set of Azure credits. One of the first things I wanted to do was rebuild my Azure lab environment. This time, I wanted to do it the right way. I wanted it to mirror how I would design and deploy a real enterprise environment, including running fully on private endpoints and following a proper hub-and-spoke network model.
Just as importantly, I wanted everything defined in Infrastructure as Code (IaC) so I could spin environments up and down whenever I needed. That also aligns perfectly with what my team at Jamf is working on right now. We are making some changes to our underlying Azure architecture, including deeper network isolation, security controls, intergration with Jamf security cloud security products, and a shift from Bicep to OpenTofu. We will also be using AI agents to do a lot of the heavy lifting in that refactor. I will be sharing more about that in future blogs and talks as much as I am able to publicly.
Because OpenTofu is at the center of that work, I decided to build my entire Azure lab using OpenTofu and a full hub-and-spoke architecture. This gives my team a real, working reference base implementation that we can build on for production designs. I also want to share this with the larger tech community.
If you are note familiar with OpenTofu it is an open source infrastructure-as-code engine based on Terraform that lets you define, deploy, and manage cloud infrastructure using declarative configuration files, and you can learn more at https://opentofu.org.
The solution deploys a production-style Azure network and platform foundation that includes:
Hub VNet with Azure Firewall, VPN Gateway, and DNS Private Resolver
Spoke VNet with peering and default routes through the firewall
Key Vault and Azure Container Registry using private endpoints
Optional Jumpbox VM for secure management access
GitHub Actions CI/CD pipeline using OIDC authentication
How the Automation Works
This is a multi-part solution built around a bootstrap Bash script (bootstrap.sh) and a fully generated OpenTofu repository.
The bootstrap script creates everything you need to get started:
It creates an Azure Storage Account to store your OpenTofu remote state.
It generates a complete OpenTofu project, including modules, variables, and environment structure.
It configures the backend so OpenTofu uses Azure Storage for state.
It creates a ready-to-use GitHub Actions pipeline for CI/CD.
Once the repository is generated, you can deploy your Azure environment by running OpenTofu locally or by pushing the repo to GitHub and letting the pipeline handle deployments for you. Within minutes, you can have a fully functional Azure hub-and-spoke environment up and running, and you can customize the generated modules to fit your own requirements.
Deployment Modes
The bootstrap bash script supports two deployment modes depending on how advanced and locked-down you want the environment to be.
FULL Mode (Default) This is the enterprise-grade option.
Hub VNet with Azure Firewall, VPN Gateway, and DNS Private Resolver
Spoke VNet with peering and default route through the firewall
Private endpoints for Key Vault and Azure Container Registry
Optional Jumpbox VM for secure management
GitHub Actions CI/CD pipeline with OIDC authentication
BASIC Mode This is a simpler version for learning or labs.
Hub VNet with Azure Firewall only
Spoke VNet with peering and default route through the firewall
Public access for Key Vault and Azure Container Registry
No Jumpbox, VPN Gateway, or DNS Private Resolver
GitHub Actions CI/CD pipeline with OIDC authentication
What the bootstrap.sh Script Does
When you run the bootstrap script, it will:
Prompt you to select FULL or BASIC deployment mode
Create an Azure Storage Account for OpenTofu remote state in rg-tfstate
Generate the full OpenTofu repository structure based on your choice
Configure the OpenTofu backend to use the storage account
Create GitHub Actions workflow files for CI/CD
Output the storage account details and the GitHub secrets you need to configure
From there, you are ready to deploy and customize the script and OpenTofu based on your Azure hub-and-spoke environment entirely through code.
Here is the Readme from the repo. It goes even more in depth into my “OpenTofu Azure Hub and Spoke” solution. I hope you find it useful!
This repository contains a production-ready, modular OpenTofu configuration that deploys Azure hub-spoke network topology with two deployment modes (private or public) to match your requirements and budget.
Architecture Overview
This solution deploys a hub-and-spoke network architecture (visual shows full-private deployment):
Enterprise-grade Azure network architecture lab environment with Site-to-Site VPN, Azure Firewall, DNS Private Resolver, and core services
This repository contains a production-ready, modular OpenTofu (Terraform) configuration that deploys a complete Azure hub-spoke network topology designed for hybrid cloud scenarios, connecting your on-premises network (e.g., UniFi network) to Azure.
Architecture Overview
This lab deploys a hub-and-spoke network architecture following Azure best practices (visual shows full private deployment):
Last week Docker made a big move for the container ecosystem. Docker Hardened Images (DHI) are now free and open source, making secure container foundations accessible to everyone.
If you build, deploy, or operate containerized workloads, this is one of those changes that quietly but meaningfully improves day to day security and reliability.
Let’s break down what Docker Hardened Images are, why they matter, and how you can start using them today.
What Are Docker Hardened Images?
Docker Hardened Images are base container images that come pre-hardened for security and transparency. Instead of starting from a generic base image and layering on your own security practices, DHI gives you a safer starting point out of the box.
They are designed to reduce common container risks without adding operational overhead or complexity.
In practical terms, this means Docker has already done the work many teams struggle to keep up with.
What You Get Out of the Box
When you use Docker Hardened Images, your base images now:
Include automated security metadata
Are minimalist and optimized for faster builds and startup times
Contain significantly fewer known vulnerabilities (CVEs) from the start
Are fully free and open source
This shifts container security left, right to the foundation of your application images.
There still is a paid version of Docker Hardened Images for those that have enterprise needs. Here is a breakdown of what you get with the Free Docker Hardened Images and the Paid version.
Why This Is a Big Deal
Most container vulnerabilities originate from base images. Teams often inherit outdated packages, unused libraries, or poorly maintained dependencies without realizing it.
Docker Hardened Images help address that by:
Reducing the attack surface before you write any application code
Improving transparency into what is inside your images
Lowering the burden on platform and security teams
Making secure defaults accessible even to small teams and solo developers
Security becomes the baseline rather than an afterthought.
As many of you know, I was honored to be named a Docker Captain earlier this year (2025). This week, I had the incredible opportunity to attend my very first Docker Captain Summit, and what an experience it was.
The event reminded me a bit of the Microsoft MVP Summit, but with even closer access to the Docker product teams across multiple areas. Every year, the Captain Summit takes place in a different location, bringing together Docker staff from product groups, community management, marketing, and DevRel, along with fellow Docker Captains from around the world.
At the summit, we got an inside look at Docker’s roadmap and were among the first to learn about upcoming products and initiatives. We also had the opportunity to provide direct feedback to the product teams, helping shape the future of Docker from the community’s perspective.
This year’s summit was held in Istanbul, and it was a fantastic few days of connecting with so many brilliant people. I finally met in person several Docker staff members and Captains I’ve been collaborating with online. It was also a chance to reunite with friends from Microsoft and the MVP community.
Of course, not everything we discussed can be shared publicly because of NDAs, but I can tell you that we all walked away with some exciting insights and some awesome Docker swag.
