🚀 MLOps Workshop
From Notebook to Production: Complete MLOps Journey in 60 Minutes
⏱️ 60 Minutes
🚀 Hands-on
☁️ Cloud Native
7
Workshop Phases
60
Minutes Total
30+
Resources
100%
Hands-on
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Pipelines
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Pipelines - Build and run ML workflows
Workshop Overview
This workshop takes participants through the complete MLOps journey in 60 minutes: starting with cloud resources setup, moving into building and running Kubeflow pipelines for training and validation, packaging and deploying models with KServe, monitoring inference endpoints for drift and latency, and finally enabling retraining triggers with CI/CD integration.
🎯 Learning Objectives
- • Understand MLOps principles and practices
- • Build end-to-end ML pipelines
- • Deploy models at scale with KServe
- • Implement monitoring and alerting
- • Set up automated retraining workflows
🛠️ Technologies
- • Kubernetes & Kubeflow
- • KServe for model serving
- • Prometheus & Grafana
- • GitHub Actions & Argo
- • MLflow for model registry
📋 Prerequisites
- • Basic Kubernetes knowledge
- • Python programming experience
- • Cloud platform account (GCP/AWS/Azure)
- • Docker and kubectl installed
- • Git and GitHub account
🚀 Quick Start
Ready to begin your MLOps journey? Start with the first phase:
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