Practicus AI Operations Tutorial
In this demo we will focus on the operational side of Practicus AI. This tutorial will guide you through how to manage, monitor, and maintain the platform in production at scale. Whether you're running Practicus AI on a public cloud Kubernetes environment such as AWS EKS, Azure AKS, Google GKE, or on-premises solutions like Red Hat OpenShift or Rancher, understanding these operational best practices ensures a stable, scalable system.
Overview
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Cloud-Native & On-Prem Flexibility
Practicus AI is fully cloud-native and can be deployed across various Kubernetes-based environments—from major cloud providers to on-premises clusters. -
Observability & Monitoring
Ability to track logs, metrics, events, and errors across your Practicus AI deployments. By leveraging add-on services like Grafana, you can create real-time dashboards and alerts to ensure continuous uptime and optimal performance. -
Workflow & Scheduling
Airflow integration provides a robust solution for scheduling and automating complex data pipelines. In an enterprise setting, these workflows often involve cross-team or cross-department coordination—this tutorial shows you how to manage and monitor such tasks seamlessly. -
Security & Compliance
As part of day-2 operations, you’ll need to ensure that your deployments adhere to security best practices. This includes understanding Kubernetes namespace isolation, role-based access control (RBAC), and any compliance measures your organization must meet.