Think Different

If you already have a Docker Compose based application. And you may want to orchestrate the containers with Kubernetes. If you are new to Kubernetes then you can search various articles in this blog or Kubernetes website. Here's a step-by-step plan to migrate your Docker Compose application to Kubernetes: Step 1: Create Kubernetes Configuration Files Create a directory for your Kubernetes configuration files (e.g., k8s-config). Create separate YAML files for each service (e.g., api.yaml, pgsql.yaml, mongodb.yaml, rabbitmq.yaml). Define Kubernetes resources (Deployments, Services, Persistent Volumes) for each service. Step 2: Define Kubernetes Resources Deployment YAML Example (api.yaml) YAML apiVersion: apps/v1 kind: Deployment metadata:   name: api-deployment spec:   replicas: 1   selector:     matchLabels:       app: api   template:     metadata:       labels:         app: api     spec:...

photo by pixabay Here's a breakdown of the key differences between Kubeflow and Airflow , specifically in the context of machine learning pipelines, with a focus on Large Language Models (LLMs): Kubeflow vs. Airflow for ML Pipelines (LLMs): Core Focus: Kubeflow: Kubeflow is a dedicated platform for machine learning workflows. It provides a comprehensive toolkit for building, deploying, and managing end-to-end ML pipelines, including functionalities for experiment tracking, model training, and deployment. Airflow: Airflow is a general-purpose workflow orchestration platform. While not specifically designed for ML, it can be used to automate various tasks within an ML pipeline. Strengths for LLMs: Kubeflow: ML-centric features: Kubeflow offers built-in features specifically beneficial for LLMs, such as Kubeflow Pipelines for defining and managing complex training workflows, Kubeflow Notebook for interactive development, and KFServing for deploying trained models. Sca...