Title

Automation of Machine Learning Workflows with OCI and Argo Workflows

About Or Title

About This Workshop

Desc Long

This hands-on lab demonstrates the integration of Oracle Cloud Infrastructure (OCI) services with Argo Workflows to create a fully automated, end-to-end machine learning pipeline. Designed for scenarios such as medical imaging analysis, the lab walks you through the process of managing raw and processed data in OCI Object Storage, automating workflows with Argo Workflows, and deploying OCI Functions and Events to trigger workflows based on real-time data changes.

The lab also includes a comprehensive Terraform script for infrastructure deployment, provisioning the necessary OCI infrastructure, including Kubernetes clusters, object storage buckets, and the associated network configurations. Participants will learn how to customise Python scripts with variables for namespaces during deployment and configure webhook triggers to automate workflow execution. By the end of the lab, you will have a robust and scalable machine learning pipeline integrated with OCI, ready to handle real-world workloads.

Workshop Length

4 hours

Outline

Lab 1 - Setting Up the Cloud Environment with Terraform
Lab 2 - Building the Machine Learning Pipeline
Lab 3 - Automating Workflow Triggers with OCI Events and Functions

Prerequisites

Basic understanding of Oracle Cloud Infrastructure (OCI) services, including Object Storage, OCI Functions, and OCI Events
Familiarity with Kubernetes concepts and tools (e.g., kubectl)
Knowledge of Docker for creating and managing containerized applications
Experience with Terraform or other Infrastructure-as-Code (IaC) tools
An active Oracle Cloud Infrastructure (OCI) account with permissions to create and manage:
Object Storage buckets
OKE (Oracle Kubernetes Engine) clusters
OCI Events and Functions
OCI IAM Policies

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