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.
MLOps
Machine Learning and AI Consulting
End-to-end consulting for ML and AI initiatives, from use-case validation to production-ready workflows and stakeholder adoption.
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.
Local to Multi-Node: Packaging ML Code with Docker
How to build lean Docker images for ML workloads without carrying notebook-era assumptions into production clusters.
Setting Up a Clean CI/CD Pipeline for Python ML Packages
How to structure testing, packaging, and trusted publishing for Python ML libraries without turning your release workflow into a bottleneck.