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Section 1. Key failure modes and initial responses

Operational guidelines: incident triage and rollback playbook

Detection and alerting basics for outages and drift

ROI checks: when costs exceed value and what to adjust

Policy violations: escalation paths and documentation

Section 2. Recovery, governance, and prevention practices

Recovery, governance, and prevention practices

🎮 Pixel Quest: Operational Failure Modes Challenge

Knowledge Check

Quiz: Incident Triage and Response

Quiz: Detection and Drift

Quiz: Financial Operations and ROI

Quiz: Policy and Compliance

Quiz: Governance and Prevention

Course Feedback

Course Evaluation Survey

Operational AI: Scaling from Pilot to Sustainable Production is a practitioner-focused course designed to help teams move beyond prototypes and keep AI systems reliable, safe, and cost-effective at scale. If you’re grappling with outages, model drift, unexpected bills, or compliance questions, this course gives you the playbooks and metrics to run AI like a production service—not a science experiment. You’ll learn incident triage stages, rollback triggers, alerting patterns, and post-incident documentation that stands up to audits. We go deep on detection and drift, covering data distribution monitoring, hallucination impact, and noise-reduced alerting strategies. You’ll also master ROI checkpoints to ensure your AI delivers measurable business value, with clear decision criteria for optimization or deprecation. Governance isn’t an afterthought: we connect recovery with sustainable risk management, showing how to institutionalize lessons learned so issues don’t repeat. Through concise modules, hands-on examples, and a playful Pixel Quest challenge, you’ll build confidence running AI in production with rigor—balancing speed, safety, and spend. Whether you manage LLM apps, recommendation systems, or predictive models, you’ll leave with actionable templates and processes to scale responsibly and keep stakeholders’ trust.

Operational AI: Scaling from Pilot to Sustainable Production

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            <p>Transitioning an Artificial Intelligence model from a controlled pilot environment to a full-scale production system presents a unique set of technical and organizational challenges. While initial success often focuses on model accuracy and proof-of-concept, sustainable production requires a rigorous focus on operational stability, cost-efficiency, and risk mitigation. This course provides a comprehensive framework for managing the lifecycle of operational AI, addressing the critical phase where experimental prototypes must evolve into reliable, value-driven business assets. Participants will explore the fundamental failure modes that threaten AI systems and the structured responses necessary to maintain service integrity in high-stakes environments.</p>

<p>Throughout this curriculum, learners will gain practical insights into incident triage, rollback strategies, and the detection of subtle performance issues such as model drift and data distribution shifts. Beyond technical monitoring, the course examines the financial and ethical dimensions of AI operations, including ROI analysis and the management of policy violations related to bias and privacy. By bridging the gap between immediate incident recovery and long-term governance, this program equips early-career professionals and corporate staff with the tools to institutionalize lessons learned and build resilient AI infrastructures. The ultimate goal is to foster a sustainable operational environment where AI consistently delivers value while adhering to strict compliance and safety standards.</p>
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