Delivering a complex distributed platform is only part of the challenge; helping teams operate it confidently is just as important. Realizing this, I decided to create an Operations training program aimed at turning technical know-how into real-world expertise. Whether you are building a distributed system or training teams to operate one, this article will help you avoid pitfalls, save time, and increase the impact of your efforts. Giving teams access to such a platform isn’t enough. They need to feel comfortable navigating through its complexities. My training aims to bridge the gap between what they know in theory and how to actually apply it. Teams should not only know how to use the platform but also be able to tackle issues, boost performance, and adapt as things change.
To this end, I draw on my experience with ArmoniK, the in-house-developed open-source distributed computing platform I work on, to share how I approached the design, implementation, and continuous improvement of such a program. Although ArmoniK serves as the context, I believe that the lessons learned apply broadly to anyone building training programs for advanced software systems.
Why organize an operations training?
Modern distributed systems are powerful, flexible, and complex. This complexity comes from the fact that they contain many moving parts: services, databases, message queues, caches, load balancers, schedulers, network overlays, and storage, each with its own failure modes and configuration surface. These components might fail independently and concurrently, producing asynchronous behavior that make debugging non-trivial. This observation is only the tip of the iceberg; if you account for network variability, heterogeneous deployment topologies, and fragmented observability, the picture is even more intricate. Hence, the true value of a modern distributed system emerges only when teams know how to deploy, monitor, and troubleshoot them autonomously. Documentation plays a key role in this endeavor, but it is insufficient on its own since it captures intended behavior but not dynamic realities. Furthermore, documentation doesn’t convey tacit operator knowledge (heuristics, mental models, and trade-offs) that experienced engineers use in incidents. Therefore, teams need practical experience, guided learning, and structured exercises that reflect real-world operational challenges.
This is why an operations training program can be a good option as it serves the following key goals:
- Build confidence and autonomy among users and support teams: Training equips participants with hands-on experience and clear workflows so they can operate the system independently, troubleshoot common issues, and make informed decisions without waiting for specialist help.
- Ensure consistent, repeatable operational practices: By teaching common procedures and checklists the training minimizes variation in how tasks are performed, which reduces errors, speeds onboarding, and makes outcomes predictable across teams and environments.
- Foster a shared understanding of system behavior and terminology: The training aligns users on key concepts, metrics, and failure modes so engineers, operators, and business users interpret logs, alerts, and documentation the same way and communicate effectively during incidents.
- Reduce dependency on experts for day-to-day operations: With practical knowledge transfer, routine maintenance, deployments, and incident responses can be handled by trained staff, reserving experts for complex design or escalations.
In my case, the ArmoniK training was designed not only to accelerate customer adoption, but also to strengthen the consultants I work with and their ability to support future clients in production environments: it combined customer-facing labs with consultant-focused troubleshooting scenarios so consultants gain repeatable support patterns, institutional knowledge, and confidence to manage live deployments.
Target audience of the training
I identified two main audiences:
- Customer teams deploying ArmoniK to orchestrate their workloads: They needed to understand how to operate the system reliably in their own environments — including upgrades, scaling, and monitoring.
- Internal consultants who support clients post-deployment: They needed the same operational expertise, in addition to the ability to troubleshoot, replicate issues, and provide guidance remotely.
This dual perspective shaped my training philosophy: every exercise and explanation had to be both instructive and reproducible, regardless of whether it was run in a cloud environment (where resources and services are accessed over the internet) or on premises (where infrastructure is physically located within an organization's own facilities).
How I built the training
From the start, I decided the training should be hands-on and experience-driven, not a theoretical walkthrough. Each session was built around realistic operational scenarios, from basic deployment to incident response. Rather than listing every feature, the focus was on how to think when running and maintaining the system.
To keep it adaptable, I organized the program in progressive stages, covering about half-day workshops that could be sequenced across a single week or spread over multiple sprints:
- Foundational concepts and architecture: Introduces system components, data flows, and design principles so participants understand how pieces interact and why configuration choices matter.
- Core operational workflows (deployment, configuration, authentication, scaling): Teaches repeatable procedures for installing and configuring the system, performing deployments, and how I implement the autoscaling of resources in the application and how they can change its configuration.
- Advanced topics such as upgrades, rollbacks: Covers safe upgrade paths, schema or API migrations, and tested rollback strategies to minimize downtime and data loss during version changes.
- Monitoring, Troubleshooting and postmortem analysis: Shows how to instrument systems, interpret metrics and logs, diagnose incidents to identify root causes and preventive actions.
This progression allowed participants to apply new knowledge directly, reinforcing understanding through practice.
Preparing the training environment
Designing the training sessions involved more than slides and demos, the most complex task was building reliable, hands-on programming labs. A complex distributed computing platform like ArmoniK has strict technical installation prerequisites. Depending on organizations' security policies, participants often cannot install arbitrary software on their workstations, and network restrictions or firewall rules might block required components. Providing ready-to-use environments was therefore essential.
To overcome this, I provisioned a dedicated cloud infrastructure for the training sessions:
- Using Packer, I built custom Linux images preconfigured with all dependencies.
- With Terraform, I deployed virtual machines from images built with Packer, assigning appropriate permissions based on each participant’s role.
This “infrastructure-as-code” approach brought three major advantages:
- Consistency: every participant received an identical, working setup.
- Reproducibility: configurations were version-controlled and easy to recreate.
- Flexibility: I could spin up new environments on demand for last-minute participants.
By automating environment setup, I allowed participants to focus entirely on learning instead of troubleshooting infrastructure.
Challenges encountered during the training
Even with careful planning, live technical training might not go as smoothly as planned. Several unexpected challenges tested my flexibility and problem-solving skills, providing insight into the realities of running distributed computing training sessions and teaching valuable lessons for future sessions:
Choose cloud components carefully
Early in the training, I used Amazon MQ for messaging exercises, only to realize afterward that SQS would have been a more cost-effective solution. This highlighted the importance of reviewing cloud service choices for both functionality and cost, especially when scaling labs for multiple participants, and the value of setting up cost alerts to prevent unexpected billing.
Be prepared for dependency changes
Last-minute updates also posed challenges. One night, a new version of the Helm provider for Terraform was released, changing how blocks must be declared and rendering my pre-written modules incompatible. Rapid troubleshooting and adaptation ensured that participants could continue without delay. Nevertheless, the incident reminded me that pinning versions and validating updates before each session is essential.
Optimize for iteration speed
Some exercises required full redeployments, which slowed progress. I later learned that targeted updates of individual components were faster and more engaging for participants.
Validate experimental features beforehand
Untested features occasionally failed to perform as planned, forcing me to improvise solutions during live sessions. This reinforced the importance of validating experimental features thoroughly before including them in training exercises.
Balance content and timing
Finally, I sometimes overestimated session durations, leaving participants with idle time. While less critical than errors or downtime, this highlighted the need to pace sessions carefully, keeping engagement high while covering all necessary material.
The value for my organization
Running these trainings produced benefits far beyond the sessions themselves.
- Improved customer relationships: participants developed a shared operational language, making collaboration and support smoother.
- Faster feedback loops: training surfaced usability issues, unclear documentation, and potential bugs, helping me improve the platform.
- Stronger internal expertise: the consultants I work with became more autonomous and confident when supporting clients.
- Increased trust: customers appreciated the transparency and gained confidence in the maturity of the product.
- Better documentation: constructing the training support material and incorporating customer feedback pushed me to improve the documentation of the platform.
In essence, the training turned operational challenges into opportunities for continuous improvement, both for the teams I support and for the platform itself.
Conclusion
Designing an effective operations training for a complex distributed system requires more than technical knowledge. It demands clarity of purpose, reproducible environments, adaptability, and a feedback-driven mindset. My experience with ArmoniK confirmed that hands-on, scenario-based learning is the most powerful way to help teams master operational excellence. Whether you’re training clients or internal engineers, the key is to provide an environment where participants can experiment safely, fail productively, and build confidence in managing real-world systems. Through this approach, operations training becomes more than a support activity; it becomes a strategic tool for scaling expertise, reliability, and trust. If you are preparing your own training, start by clarifying the outcomes you expect, prototype the environments early, and iterate with your future participants—your next session will be better for it.
