Streamlining the machine Learning lifecycle
The v1 release delivers some new functionalities and features to streamline the machine learning lifecycle.
ProductMLOps
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Practical guides to building, running, and improving ML and AI in production.
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Troubleshooting Kubernetes for data-scientists
Troubleshooting machine learning (ML) and deep learning(DL) workload on Kubernetes for data-scientists and machine learning engineers.
KubernetesGuides
Common Git commands for data-scientists
Common Git commands for data-scientists and machine learning engineers
GitGuides
Common Docker terms for data-scientists
Common Docker terms for data-scientists and machine learning engineers
DockerGuides
Machine learning workload on Kubernetes
Scheduling and orchestrating machine learning (ML) and deep learning(DL) workload workload on Kubernetes.
KubernetesGuides
Common Git terms for data-scientists
Common Git terms for data-scientists and machine learning engineers
GitGuides
Docker for data-scientists
Basic Docker concepts for data-scientists and machine learning engineers
DockerGuides
Kubernetes for data-scientists
Basic concepts of Kubernetes for data-scientists and machine learning engineers to manage machine learning (ML) and deep learning(DL) workload and applications.
KubernetesGuides
Git for data-scientists
Basic Git concepts for data-scientists and machine learning engineers
GitGuides
Polyaxon v0.5
Today, we are pleased to announce the v0.5 release of our MLOps platform, a stable version which brings significant improvements to Polyaxon’s deployment and management experience, model management and Machine Learning (ML) workflows automation.
ProductAnnouncements
New versioning, roadmap, resources
Today we are not announcing a new release of our Product, instead we would like to update our users and community about some changes and short-term roadmap of our product.
Announcements
Polyaxon v0.4.3: stable — Improved dashboard, setup, and documentation
Today, we are pleased to announce the v0.4.3 release of our MLOps platform, a stable version with improved functionalities and documentation.
ProductAnnouncements
Close exposed Kubernetes and container surfaces
Reduce Kubernetes attack surface by inventorying public services, protecting management endpoints, isolating workloads, and continuously testing exposure.
KubernetesSecurityContainers
Polyaxon v0.4.2: Kubeflow, Horovod, and MPI integrations
Polyaxon now integrates with Kubeflow, Horovod, and MPI natively.
ProductAnnouncements
Polyaxon v0.4: Integrations, Machine Learning CI, Better support of external repos, Better support of cloud storages...
Polyaxon v0.4 added integrations, machine learning CI, experiment tracking, cloud storage support, TPU scheduling, and workflow improvements.
ProductAnnouncements
Polyaxon 0.2.7: experiments comparison & model analysis
Today’s release is the first step towards experiments comparison on Polyaxon and overall model analysis.
ProductAnnouncements
Install Polyaxon on MicroK8s
This is a guide to assist you through the process of setting up a Polyaxon deployment using MicroK8s.
KubernetesGuides
Install Polyaxon using kubeadm on Kubernetes
This is a guide to assist you through the process of setting up a Polyaxon deployment using kubeadm and Kubernetes.
KubernetesGuides
Install Polyaxon on Minikube
This is a guide to assist you through the process of setting up a Polyaxon deployment using Minikube.
KubernetesGuides
Polyaxon 0.2.3: Experiment stability, better tracking, better visualization
We’re pleased to announce the new release of Polyaxon 0.2.3. Today’s release continues to focus on internal improvements and stability, and make the best experience to run and scale machine learning on Kubernetes.
ProductAnnouncements
Polyaxon v0.2
The new version of Polyaxon comes with a lot of new features, bug fixes, and performance enhancement.
ProductAnnouncements
CartPole game by Reinforcement Learning, a journey from training to inference
This project is intended to play with CartPole game using Reinforcement Learning and to know how we may train a different model experiments with enough observability (metrics/monitoring). The model is divided basically in three parts: Neural network model, QLearning algorithm and application runner
GitHub
Mounting multiple data and outputs volumes
For some advanced uses cases, users might need to mount more than one data and/or outputs volumes.
Product
Autoscaling of preemptible GPU resources
Discussion about how to use a pool of preemptible GPUs that are automatically used whenever experiments or a hyperparameter search is created in Polyaxon.
Guides