Operationalizing AI: From Prototype to Production

Building an AI model is the easy part—making it work reliably in production is where the real engineering begins. In this fast-paced, experience-driven session, Ken explores the architecture, patterns, and practices behind operationalizing AI at scale. Drawing from real-world lessons and enterprise implementations, Ken will demystify the complex intersection of machine learning, DevOps, and data engineering, showing how modern organizations bring AI from the lab into mission-critical systems.

Attendees will learn how to:

Design production-ready AI pipelines that are testable, observable, and maintainable

Integrate model deployment, monitoring, and feedback loops using MLOps best practices

Avoid common pitfalls in scaling, governance, and model drift management

Leverage automation to reduce friction between data science and engineering teams

Whether you’re a software architect, developer, or engineering leader, this session will give you a clear roadmap for turning AI innovation into operational excellence—with the same pragmatic, architecture-first perspective that Ken is known for.

Incident Command for Engineering Leaders

Most teams treat incidents as technical failures. Great teams treat them as coordination failures under stress. This session gives engineering leaders a practical incident command system they can apply immediately: roles, communication cadence, decision logging, escalation paths, and postmortems that create learning instead of fear.

When incidents hit, technology matters — but leadership determines outcomes. This session walks through an operating model for incident response that scales across teams and time zones without chaos.

We cover clear roles (incident commander, comms lead, operations lead, and scribe), fast status loops, and decision frameworks that lower risk under pressure. You’ll see practical templates for timeline capture, stakeholder communication, and recovery prioritization.

We also cover the most ignored part: after-action learning. You’ll leave with a blameless postmortem structure that improves systems, process, and team behavior instead of assigning guilt.

Includes realistic scenarios, facilitation techniques for cross-functional pressure moments, and a leadership checklist you can use in your next production incident.

Outcomes:

• Reduce time-to-understand and time-to-recover
• Communicate clearly with executives and customers
• Build operational maturity without blame culture
• Replace panic theater with dependable response habits

No panic theater. Just practical leadership patterns that work when production is on fire and Slack has gone feral.

How to Measure Success: Making OKRs and KPIs Actually Work

Most teams track numbers—but few measure what truly matters. In this insightful and practical session, Ken Sipe breaks down how to align organizational goals with measurable outcomes using Objectives and Key Results (OKRs) and Key Performance Indicators (KPIs) that actually drive success.

Ken will cut through the buzzwords and frameworks to show how engineering teams, product leaders, and executives can build a measurement culture that’s both data-driven and purpose-aligned. You’ll learn how to define metrics that motivate, avoid vanity indicators, and establish traceability from daily work to strategic outcomes.

Key takeaways include:

How to craft meaningful OKRs that align technical and business goals

The difference between activity, output, and outcome metrics—and why it matters

Techniques for cascading objectives across teams without creating chaos

How to use metrics as a feedback system, not a weapon

Real-world examples of OKRs and KPIs that improved clarity, accountability, and results

Whether you’re scaling an engineering organization or trying to bring more focus to your current team, this session will help you turn measurement into momentum—and ensure that success isn’t just tracked, but achieved.

The Operating Limits of Microservices

Reliable systems are not accidents. They are designed with explicit operating limits. This session translates lessons from high-risk domains into practical engineering guardrails for microservices: latency budgets, timeout strategy, retry discipline, concurrency limits, and blast-radius controls.

In high-consequence systems, teams define and respect operating limits. Software teams should do the same.

This session introduces an operating-limits model for modern microservices and platform environments. We’ll map common failure patterns (retry storms, cascading timeouts, queue overload, dependency fan-out) to concrete design and operational constraints that prevent small issues from becoming full incidents.

You’ll learn practical techniques for timeout layering, bulkheads, error budgets, load shedding, progressive degradation, and observability signals that reveal approaching limits before customers feel impact.

We’ll also cover leadership practices: how to align teams around reliability contracts and how to enforce guardrails without turning architecture into bureaucracy.

Outcomes:

• Define service-level operating envelopes
• Reduce cascading failures in distributed systems
• Improve incident prevention with better guardrails
• Balance delivery speed with reliability discipline

Yes, we will talk about when your retries are lying to you. And no, adding one more queue is not always the answer.

Lead Don't Manager

In the fast-paced world of software delivery, we often mistake “management” for “leadership.” Management is about complexity, stability, and the coordination of resources; leadership is about change, alignment, and the inspiration of people. For many tech leaders, the instinct is to manage the code, the deadlines, and the tickets—but great systems aren't built by managed workers; they are built by led innovators.

In this session, Ken Sipe explores the critical shift from a command-and-control mindset to a high-trust leadership model. We will dive into the “Anti-Patterns of Management” that stifle creativity and slow down velocity, and replace them with actionable leadership strategies designed specifically for technical teams.