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1. Running Machine Learning Workloads on Kubernetes with Google AI Platform and TensorFlow Serving

   In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.

   2024-05-15 Tags: gcp, kubernetes, machine learning, tensorflow sing, deployment, inference, mlops, production engineering by klotz
2. CI/CD Pipelines for Machine Learning

   • Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
   • Importance of CI/CD in ML lifecycle
   • Designing CI/CD pipelines for ML models
   • Automating model training, deployment, and monitoring
   • Overview of tools and platforms used for CI/CD in ML

   2024-05-08 Tags: mlops, cicd, machine learning, model training, production engineering, monitoring, automation, qwak, github actions, docker, kubernetes, argocd by klotz
3. MLOps — A Gentle Introduction to Mlflow Pipelines

   This article provides an introduction to Mlflow, an open-source platform for end-to-end machine learning lifecycle management. The article focuses on using MLflow as an orchestrator for machine learning pipelines, explaining the importance of managing complex pipelines in machine learning projects.

   2024-04-24 Tags: mlflow, machine learning, mlops, pipeline, orchestration, lifecycle, llm by klotz

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