Deploying an AI model is only the beginning—keeping it reliable, explainable, and impactful in production requires strong MLOps skills. In this course, learners apply best practices to orchestrate the deployment lifecycle using continuous integration, continuous delivery, and tools like GitLab and Kubernetes. They analyze real telemetry data to investigate error spikes, trace root causes, and resolve performance issues with monitoring platforms such as Kibana. Finally, learners evaluate whether deployed models deliver on technical and business goals, comparing KPIs like conversion lift against targets and recommending next steps. Through guided labs, case studies, and discussions, learners gain practical experience in deploying, diagnosing, and evaluating AI systems with confidence.
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December 2025
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There is 1 module in this course
Deploying an AI model is only the beginning—keeping it reliable, explainable, and impactful in production requires strong MLOps skills. In this course, learners apply best practices to orchestrate the deployment lifecycle using continuous integration, continuous delivery, and tools like GitLab and Kubernetes. They analyze real telemetry data to investigate error spikes, trace root causes, and resolve performance issues with monitoring platforms such as Kibana. Finally, learners evaluate whether deployed models deliver on technical and business goals, comparing KPIs like conversion lift against targets and recommending next steps. Through guided labs, case studies, and discussions, learners gain practical experience in deploying, diagnosing, and evaluating AI systems with confidence.
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7 videos3 readings4 assignments
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