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.
Friday, September 18, I’ll be speaking at Afrofest Tech Day in Golden Valley, Minnesota.
I’m looking forward to joining other technology leaders, entrepreneurs, professionals, and members of the community for a day focused on innovation, technology, and opportunity.
My session is titled “AI The Bigger Picture”
AI continues to dominate conversations across nearly every industry. New models, tools, agents, copilots, and platforms seem to launch every week. For business leaders, that can make it difficult to separate what is genuinely useful from what is simply the latest trend.
That is exactly what I want to address in this session.
Rather than focusing on futuristic predictions, I’ll talk about how businesses can use AI right now. The goal is to make AI practical and approachable, especially for organizations trying to understand where it can actually create business value.
Understanding the AI Landscape
We’ll start by breaking down the current AI landscape in plain English.
There are general-purpose AI assistants such as ChatGPT, Claude, Gemini, and Copilot, along with specialized tools for writing, research, software development, images, video, automation, and enterprise workflows. One of the key messages from the session is that businesses do not need to adopt every new AI product that appears.
In many cases, the best place to start is with the technology your organization already uses.
AI is increasingly being built directly into tools such as Microsoft 365, Excel, Outlook, QuickBooks, Salesforce, Google Workspace, Slack, Confluence, and other business platforms. For many organizations, their first meaningful AI use case may not require purchasing an entirely new platform at all.
From Using AI to Building With AI
I’m also going to spend some time on vibe coding.
Vibe coding is changing who can build software. Instead of starting with programming languages and writing every line of code manually, people can increasingly describe what they want in natural language and work with AI to generate a functioning application. I will be speaking about Dala for the first time a Vibe Coding platform built by Gebeya. It can be found here: https://dala.gebeya.com.
During the session, I’ll share an example from my own work where I needed a utility to help migrate business data from Square to Odoo. Rather than searching for an existing product or spending days building something from scratch, I used AI to help create the tool, pushed it to GitHub, and deployed it within a very short period of time.
That is where I think things get especially interesting for entrepreneurs and small businesses.
AI is not only helping us write emails or summarize documents. It is beginning to lower the barrier between having an idea for a tool and actually building it.
Where Is Your Business on the AI Journey?
Another topic I’ll cover is the progression businesses typically go through as they adopt AI.
That journey can start with simple tasks such as rewriting emails and drafting content. From there, AI begins helping with more sophisticated work such as proposals, forecasting, analysis, strategy, customer onboarding, and eventually complex workflows that operate with significantly more autonomy.
Not every company needs to jump directly to autonomous AI agents.
The more important question is:
Where can AI remove friction, save time, improve decisions, or create something that would have been difficult to build before?
That is the conversation I hope attendees leave thinking about.
See You at Afrofest Tech Day
I’m excited to be part of Afrofest Tech Day and to connect with people from across the Twin Cities technology and business community. it will be at Brookview:
If you’re attending, come say hello. I’m looking forward to sharing some practical examples, learning from the other speakers, and having some good conversations about where AI is actually taking business.
Session: AI for Business Without the Hype Speaker: Steve Buchanan Date: Friday, September 18 Location: 316 Brookview Pkwy S Golden Valley, MN
Generative AI is changing how project managers, business analysts, product owners, developers, and other technology professionals approach project planning and requirements gathering. Teams can now use GenAI to organize scattered information, accelerate documentation, identify missing details, and create stronger project artifacts.
However, generating content is only part of the process. Teams also need to validate the output, maintain traceability, involve the right stakeholders, and apply human judgment before AI-generated artifacts are used to guide development.
In this intermediate-level course, I focus on practical and responsible ways to apply GenAI across project planning, requirements gathering, documentation, validation, and governance. The course is approximately 75 minutes and is part of Pluralsight’s Generative AI Across the Software Development Lifecycle learning path.
What You’ll Learn
Modern project teams are expected to move quickly while still producing clear plans, accurate requirements, and documentation that stakeholders can trust.
This course explores how GenAI can help teams meet those expectations without removing people from the decision-making process.
Throughout the course, you’ll learn how to:
Generate initial project charters and define project scope
Identify assumptions, constraints, dependencies, and scope boundaries
Support Agile planning, backlog refinement, estimation, and release planning
Create risk registers and develop potential mitigation strategies
Transform meeting notes, interviews, emails, and informal briefs into structured requirements
Generate user stories, acceptance criteria, and definitions of done
Identify missing non-functional requirements related to security, performance, accessibility, and compliance
Detect gaps, ambiguities, and contradictions in requirements
Generate clarifying questions for stakeholder review
Establish human review and stakeholder sign-off workflows
Maintain traceability between requirements, backlog items, testing, and supporting evidence
Apply governance practices to AI-generated project artifacts
The course is intentionally tool-agnostic, with an emphasis on concepts and transferable practices that can be applied across different GenAI platforms and tools.
Applying GenAI to Project Planning, Scoping, and Risk Management
The first module explores how GenAI can support early project planning activities.
I demonstrate how to use GenAI to create an initial project charter, establish scope boundaries, document assumptions and constraints, and support Agile planning activities.
The module also covers how GenAI can assist with backlog refinement, effort estimation, release planning, risk identification, and mitigation planning.
For example, GenAI can help a project team identify common risks, organize them into a risk register, and propose possible contingency approaches. This can give the team a stronger starting point for planning and stakeholder discussions.
Just as importantly, I discuss the limitations of AI-generated planning artifacts.
GenAI may not understand internal politics, team dynamics, organizational history, available resources, unstated constraints, or business context that was not included in the prompt. Its output may sound complete while still missing important details.
Human judgment remains essential.
Accelerating Requirements Gathering and Documentation
The second module focuses on using GenAI to improve requirements gathering and documentation.
Requirements are often distributed across stakeholder conversations, meeting transcripts, emails, notes, whiteboards, documents, research findings, and informal requests. Important decisions may also exist only as verbal agreements or undocumented assumptions.
GenAI can help organize these inputs and transform them into structured artifacts that a project team can review and refine.
In this section, I show how GenAI can assist with:
Requirements workshops
Stakeholder discussion prompts
Meeting summaries and action items
Structured requirements documentation
User stories and acceptance criteria
Non-functional requirements
Definitions of done
Gap and ambiguity detection
Clarifying questions
Audience-adapted requirements documentation
The goal is not to have AI make final product or project decisions.
The goal is to help teams build a stronger starting point, organize information faster, ask better questions, and spend more time validating requirements with stakeholders.
Hands-On Demos with GitHub Copilot App
One of the highlights of the course is the set of practical demos.
I used GitHub Copilot App, OpenAI’s Codex, and GitHub Projects througout the demos so that you have an example with real tools you can start using today. In one of the demos GitHub Copilot App was used to move from unstructured project inputs to usable planning and requirements artifacts in GitHub Project. I show learners how GenAI can move beyond producing isolated text and become part of a real project workflow.
Across the demos, I show how to use GitHub Copilot to:
I recently had the pleasure of chatting with Brian Horowitz at Dice.com to share my thoughts on why MCP is a game-changer for developers and organizations alike. The article, titled “Model Context Protocol: What Is It and How to Learn It”, explores how MCP is helping transform AI systems from isolated chat experiences into connected systems that can interact with real-world tools, services, and enterprise platforms.
As part of the article, I shared some thoughts on how MCP is becoming the connective layer between AI agents and enterprise systems:
“It’s like an API but for AI.”
That simple comparison captures why MCP matters so much. Traditional APIs allow applications to communicate with each other. MCP extends this idea into the AI world, enabling AI systems and agents to securely connect to tools, data sources, SaaS platforms, and operational systems in a standardized way.
One of the examples I shared in the article was how MCP enables AI systems to interact directly with enterprise collaboration and workflow platforms.
As I explained in the interview:
“You can connect a platform like ChatGPT to a common tool like Slack. And if you connect MCP to Atlassian Suite, Jira or Confluence, you can prompt AI to perform tasks with those systems.”
This is one of the reasons MCP is generating so much excitement across the industry. Instead of AI being limited to answering questions in isolation, MCP allows AI agents to interact with the actual systems teams use every day.
Imagine prompting an AI assistant to:
Create or update Jira tickets
Pull information from Confluence documentation
Summarize Slack discussions
Generate status reports across engineering systems
Trigger workflows and operational tasks
That shift moves AI from being simply conversational into becoming operational.
For engineering organizations, platform teams, and enterprise IT departments, this creates major opportunities to improve productivity, automate repetitive workflows, and build smarter developer experiences across existing toolchains.
At companies operating at scale, especially those managing cloud platforms, Kubernetes environments, DevOps systems, and SaaS operations, MCP has the potential to become a foundational integration layer for enterprise AI workflows.
Why MCP Matters
One of the biggest limitations of AI systems historically has been context and actionability. AI models could generate responses, but they often struggled to interact directly with the systems where actual business work happens.
MCP changes that.
Instead of building custom integrations for every AI interaction, organizations can expose capabilities through MCP servers that AI systems can discover and use dynamically. This creates a more scalable and interoperable ecosystem for AI tooling.
In the article, I discussed examples such as:
Connecting AI systems to tools like Slack, Jira, and Confluence
Enabling AI agents to work across DevOps and IT operations workflows
Allowing healthcare systems to connect AI to scheduling, insurance, and EHR platforms
Using MCP as the “glue” between AI agents and enterprise systems
This is where things get especially exciting for cloud engineering, platform engineering, and AI infrastructure teams.
MCP and the Future of Enterprise AI
I strongly believe MCP will become foundational infrastructure for enterprise AI adoption.
As organizations move beyond isolated AI chat experiences and toward AI agents that can actually perform work, interoperability becomes critical. MCP helps provide a standard way for AI systems to securely interact with tools and data sources without requiring endless custom integrations.
We are already seeing major momentum across the industry, including adoption and support around MCP-related tooling from companies and ecosystems tied to AI platforms, developer tooling, and cloud services.
For engineering leaders, cloud teams, and developers, this is a space worth paying attention to now, not later.
How to Start Learning MCP
One of the recommendations I shared in the article was to start hands-on:
Experiment with MCP servers locally
Use tools like Docker Desktop to simplify setup
Explore AI agents connected to MCP-enabled systems
Learn foundational skills in Python and debugging tools like Visual Studio Code
Focus on understanding how AI agents interact with external systems
The best way to understand MCP is to build with it.
Final Thoughts
It’s an honor to be included alongside other industry voices discussing where AI infrastructure and interoperability are headed next.
We are entering a phase where AI is no longer just about prompts and chat interfaces. The next wave is about connected AI systems, AI agents, and enterprise integration at scale.
And MCP is quickly becoming one of the most important standards enabling that future.
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:
Ive reached a milestone with my 30th course recently published on Pluralsight. This course is titled Google Firebase Studio Foundations. This was a course topic I suggested to the teams at Pluralsight since Vibe Coding is seeing so much growth and this solution is used for that. It is my 6th AI related course. Firebase Studio is Google’s full stack AI-powered development environment that streamlines the process of prototyping and building apps from idea to deployment.
In this course, Google Firebase Studio Foundations, you’ll start by learning the basics of vibe coding with Firebase Studio. First, you’ll explore how the Gemini AI Agent fits into the development workflow. Next, you’ll discover how to speed up backend, frontend, and mobile app development with AI assistance. Finally, you’ll take an app idea from concept to a working deployment on Firebase App Hosting. By the end of this course, you’ll have the skills needed to confidently use Firebase Studio to build and run modern apps.
I brought this topic forward because I was excited about the opportunity to author a course that showcases what Firebase Studio can do in the vibe coding space. I also wanted to raise awareness about the platform since it can be used for free, and developers can expand to a generous number of workspaces at no cost through a Google Developers account. I packed this course with demos as we work through vibe coding an app.
This course is ideal for beginners and aspiring developers who want to prototype, build and deploy apps with Google Firebase Studio. Ideal learners include students, early-stage founders, and tech professionals curious about AI-assisted development.
These are the topics in the course:
Get Started with Firebase Studio
Intro and Overview
Introduction to Vibe Coding
Introduction to Firebase Studio
Demo: Exploring Firebase Studio
Development with Firebase Studio
Intro and Overview
Accelerating Development with Vibe Coding
Demo: Generating a Full App with the Firebase Prototyper
I hope this course serves as a valuable resource in your Vibe Coding, AI, and app building 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:
I posted about this milestone on LinkedIn. Something really cool happened. The former CEO and founder of Pluralsight Aaron Skonnard commented on the post congratulating me. This means a lot coming from the founder of Pluralsight.
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 my 29th Pluralsight course is now live titled Building Applications with OpenAI. This course guides developers through creating modern AI powered applications using OpenAI APIs. Whether you are just getting started with generative AI or looking to integrate it into real projects, you will walk away with practical skills you can use right away.
This was a fun course to build. In this course you will learn how to integrate OpenAI into real world applications from end to end. We begin by setting up the OpenAI API, handling authentication, and designing effective prompts. Then we build a full stack web app that uses AI to analyze and classify data while exploring best practices for deployment, performance monitoring, and error handling. By the end you will have the confidence to build, deploy, and scale your own AI driven solutions.
🧠 Why This Course Matters
Generative AI is reshaping how software gets built and developers are expected to know how to integrate these capabilities into applications. This course gives you the foundational and practical knowledge to do that. You will see how to handle prompt refinement, token limits, deployment tradeoffs, and optimization strategies.
📘 Official Course Description
“Generative AI is changing how software is developed, and developers are now expected to integrate AI features into modern applications. In this course, Building Applications with OpenAI, you’ll gain the skills to build, deploy, and maintain AI-powered web applications. First, you’ll explore how to configure the OpenAI API, manage authentication, and craft effective prompts. Next, you’ll build a full-stack expense tracking app that uses OpenAI to analyze and categorize expenses. Finally, you’ll learn how to deploy your app using platforms like Render or Google Cloud, monitor performance, and handle challenges such as token limits, error handling, and prompt optimization. When you’re finished with this course, you’ll have the knowledge and tools to confidently integrate OpenAI into your own applications and bring AI capabilities to your development projects.“
This course is a part of the “OpenAI for Developers Path” on Pluralsight. The path can be found here: https://app.pluralsight.com/paths/skills/openai-for-developers and has many courses that will teach you various aspects of bringing OpenAI into your applications.
If you’re building applications and need to add AI, this course will help you. Check out the course here:
I hope this course serves as a valuable resource in your AI 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:
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.
