Posts Tagged k8s

AI/ML on Kubernetes: Deploying Models with Pulumi on Google Cloud

AI/ML on Kubernetes: Deploying Models with Pulumi on Google Cloud

Kubernetes has transformed cloud infrastructure by enabling scalable, containerized applications. While it initially gained traction for managing web applications and microservices, its capabilities now extend to AI/ML workloads, making it the go-to platform for data scientists and machine learning engineers.

Running AI/ML workloads on Kubernetes presents unique challenges, including:

  • Specialized hardware requirements (e.g., GPUs, TPUs)
  • Scalability for model training and inference
  • Complex data pipelines that integrate various cloud services
  • Infrastructure automation for seamless deployment

Google Cloud Kubernetes (GKE) provides a robust foundation for AI/ML workloads, but managing infrastructure manually can be cumbersome. This is where Pulumi comes in—enabling Infrastructure as Code (IaC) to automate and simplify AI/ML infrastructure on Kubernetes.

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Enforcing Policy as Code on Discovered Resources with Pulumi

Enforcing Policy as Code on Discovered Resources with Pulumi

In this post, we’re introducing a powerful new capability in Pulumi Insights that extends policy as code (PaC) beyond infrastructure as code to automatically govern all cloud resources in your environment. By unifying policy enforcement across both IaC and discovered resources, you can now write policies once and apply them universally - dramatically simplifying how organizations maintain security and compliance standards at scale.

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Kubernetes Best Practices I Wish I Had Known Before

Kubernetes Best Practices I Wish I Had Known Before

Kubernetes has undeniably transformed the way we build, ship, and run applications. But let’s be honest, getting started with Kubernetes can feel like climbing Mount Everest in flip-flops.

As a cloud-native citizen and Kubernetes enthusiast, I’ve learned the hard way that there are a bunch of “wish I had known that earlier” best practices. They could have saved me time, money, and headaches.

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105 Ways to Run Containers: The Cloud Container Iceberg

105 Ways to Run Containers: The Cloud Container Iceberg

If a computer can be connected to the internet, someone has tried to run a container on it. From quantum computers to smart toasters, from phones in AWS racks to CI pipelines, there’s many ways to deploy containers. While most people only know about the mainstream cloud providers at the surface, there’s actually a vast world of increasingly unusual and specialized options beneath.

This guide serves two purposes: to showcase the surprising breadth of container options available today, and to help you understand the full spectrum of choices – from practical to very experimental. Whether you’re looking for production-ready solutions or just curious about what’s out there in the dark depths, you’ll find something interesting here.

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Fargate vs EC2

Fargate vs EC2

Building an EKS cluster requires choosing how your containers will actually run - either on EC2 instances you manage or through AWS Fargate’s pod-by-pod approach. The differences can be pretty dramatic in practice. I’m setting up a demo cluster right now using Pulumi, so let me show you what I mean.

  1. Bin Packing
  2. Pros and Cons
  3. Workload Example: Static Analysis
  4. Example: Go Services for E-commerce
  5. Fargate vs EC2 Pricing
  6. Misconceptions About Fargate
  7. Managing Container Orchestration with Pulumi
  8. Why Not Both

Here is my Fargate cluster:

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