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Utilizing GPUs efficiently in AI workloads can significantly reduce costs and improve performance. GKE offers features like multi-instance GPUs and time-sharing to optimize GPU usage. Assessing your specific GPU needs is crucial for effective resource management. Learn how to set up GKE for AI workloads and more...
Kubeflow is an open-source platform for managing machine learning workflows on Kubernetes, ensuring scalability and portability. It simplifies AI model lifecycle stages like training and deployment, featuring components for hyperparameter tuning and workflow orchestration. Discover the power of AI model lifecycle management with Kubeflow on Kubernetes...
Natural Language Processing (NLP) models are essential for AI applications, but deploying them requires MLOps tools. Discover how Pachyderm, MLflow, TFX, and Kubeflow transform NLP workflows from research to reality...
Integrating GitHub Actions with Kubernetes simplifies the continuous deployment process. Automate workflows, minimize manual steps, and enhance reliability. Reduce human errors and ensure consistent, efficient deployments with this powerful combination of tools...
Migrating from Heroku to EKS offers greater scalability and control. EKS provides enhanced security and better AWS integration. Evaluating your current Heroku environment is crucial. Docker and Helm Charts simplify deployment, and ensuring data consistency during database migration is essential to avoid data loss...
Krew is a plugin manager for kubectl, simplifying plugin management for Kubernetes. Essential plugins like kubectx, kubens, and kubectl-tree streamline workflows, enhancing productivity. Learn how tools like kubectl-debug and kubectl-access-matrix ensure a secure and efficient environment while housekeeping plugins keep operations smooth...
Migrating from VMware to Google Kubernetes Engine (GKE) automates and streamlines application management. GKE offers a managed Kubernetes experience, simplifying processes and enhancing efficiency. Assess your VMware environment and use Migrate for GKE to ease the transition and optimize resource utilization...
Zero downtime deployment ensures that your eCommerce platform remains available and responsive during updates. Learn about Kubernetes techniques like rolling updates, blue/green deployments, and canary releases to achieve seamless user experiences and maintain customer trust, even during peak seasons...
MLOps on Google Cloud Platform streamlines ML workflows by integrating development, deployment, and monitoring. Key components include Vertex AI, TFX, and Cloud Build, ensuring CI/CD of ML models, enhancing efficiency and reliability. Automating model training and deployment with GCP tools reduces human error and accelerates time-to-market...
Werf and Nelm surpass Helm by resolving Kubernetes deployment limitations. Nelm's Server-Side Apply and Werf's backward compatibility offer enhanced performance, real-time tracking, and robust updates, significantly optimizing your DevOps processes...
Kubeflow, an open-source platform on Kubernetes, simplifies ML workflows by integrating tools like Jupyter, TensorFlow, and PyTorch. It automates complex processes, enhancing scalability and collaboration in ML projects...
Kubernetes can reduce deployment time by up to 90%, drastically speeding up software release cycles. By automating infrastructure provisioning, DevOps teams focus more on coding and less on manual tasks, ensuring higher reliability and scalability. Discover how Kubernetes is revolutionizing software delivery in our latest blog post...