Michael Friedrich
Staff Developer Advocate at GitLab
Nürnberg, Germany
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Michael Friedrich is a Staff Developer Advocate at GitLab, focusing on Embedded DevSecOps and responsible AI adoption. His desire to learn how computers work led him from Hardware/Software Systems Engineering to DNS, monitoring development, and authoring Git/GitLab trainings. Michael educates at industry events and meetups. He tackles unexpected challenges, creating tutorials and live programming sessions. When not traveling, he builds LEGO models and dives into embedded hardware projects.
Area of Expertise
Topics
Confidence with Chaos for your Kubernetes Observability
Kube-prometheus has successfully deployed the Kubernetes observability stack with Prometheus, and the dashboards provide many interesting insights. What’s next? Everything is overwhelming and your teams are drowning in alerts. They need to be modified, the dashboards require more fine granular graphs, and your team discusses service level objectives (SLO). Documentation and action items for your SRE and DevOps teams are needed too.
Simulate a production incident to see whether SLOs are met, or alerts are fired. Is there a way to observe the deployed application, and see if it breaks from chaos?
Join this talk to dive into cloud-native chaos engineering, app instrumentation, and distributed tracing and learn about production incidents with failed SLOs. Gain confidence with chaos as an SRE, and as a developer seeing the value in Observability. Welcome to day 2 DevOps.
From Monitoring to Observability: Left Shift your SLOs with Chaos
Security has shifted left in CI/CD pipelines. Traditional service monitoring moved on with metrics, logs and traces and observability embraces the unknown unknowns. Developers and SREs are instrumenting applications with metrics and distributed tracing. How do service level objectives (SLOs) add to the bigger picture?
This talk invites into a developer’s tale about ops deployment scalability, availability threshold definitions and measuring application performance. What are the benefits of app instrumentation, metrics and traces and where does the journey start?
Dev becomes Ops: SLOs need to be well understood and simulated early in the development process. New building blocks come to play: Continuous Delivery, quality gates and chaos engineering - is it possible to left shift SLOs with Chaos in your CI/CD pipelines?
Attendees join for a deep dive into failure stories in the past 10 years, and learn how SLOs and chaos engineering could have helped prevent them. Follow the ideas of Prometheus changing the approach to metrics collection, Keptn innovating with quality gates and SLOs and Litmus adding chaos to the CI/CD pipeline. What are your SLOs for shifting left with chaos?
From Monitoring to Observability: eBPF Chaos
Observability with eBPF aims to help DevSecOps and SRE teams to debug and troubleshoot incidents. New event data requires storage, visualization, and verification: Do the Service Level Objectives (SLOs) match, dashboards visualize useful data correlation, network service maps make sense, and what about security policies?
Learning eBPF is hard, and needs a good approach to reduce complexity. What is the architecture, which benefits and risks are added to production environments? eBPF use cases are observability, security, and debugging production incidents. Chaos engineering helps break production to verify use cases - and can benefit from eBPF too.
This talk dives into the learning steps with eBPF and discusses Observability data collection, storage and visualization. Debug production with hands-on tools, and add chaos experiments that attempt to break eBPF probes, data collection and policies in unexpected ways - and bring new perspectives into cloud-native reliability.
Efficient DevSecOps workflows with a little help from AI
From idea to the first line of code to production deployments - DevSecOps workflows help develop software faster. There is a different level of adoption, and processes feel inefficient or block progress and innovation.
Open the DevSecOps lifecycle. Which is the most inefficient task you spend a lot of time on? Long discussions in issues, MR reviews without context, maintaining legacy code, team onboarding with new project structures, missing unit tests, analyzing the impact of security vulnerabilities, or even debugging blocking CI/CD pipelines with long stack traces…
Join this session to learn about efficient AI workflows with practical prompts and advanced practices with custom LLMs, retrieval augmented generation (RAG), and AI agents. We will also discuss how to implement AI guardrails and measure the impact of AI on DevSecOps efficiency. Get inspired to implement everything into your AI adoption plans, and join the conversation after the talk.
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