Migrating ML Workloads from EC2 to Scalable Amazon EKS
Step-by-step architectural breakdown for migrating standalone EC2 model deployments to fully orchestrated, scalable EKS clusters.
Aws
Cloud Computing: AWS, GCP, Azure, and Databricks
A cloud learning path for teams working with data engineering, ML infrastructure, and scalable analytics delivery across AWS, GCP, Azure, and Databricks.
Infrastructure as Code for Data Engineers: Terraform Basics
How to use Terraform to provision basic AWS storage and access controls for ML workloads without turning simple infrastructure into manual drift.
Introduction to Experiment Tracking without the Overhead
How to get useful experiment tracking with MLflow, cloud object storage, and minimal infrastructure instead of an oversized MLOps stack.