Blog

Protect Kubernetes workloads from DDoS attacks

Build layered Kubernetes DDoS protection with upstream filtering, edge controls, workload isolation, resource safeguards, observability, and tested response plans.

Datadog vs. AppDynamics for ML platform monitoring

Evaluate Datadog and AppDynamics against application transactions, Kubernetes infrastructure, ML workflows, telemetry governance, and operating cost.

Detect malicious activity in Amazon EKS

Detect and contain suspicious Amazon EKS behavior by correlating Kubernetes audit, AWS API, DNS, network, runtime, and workload context.

Encrypt container traffic in Kubernetes

Design encryption for Kubernetes data at rest and in transit, with workload identity, certificate rotation, key ownership, and verifiable coverage.

Kubernetes Workloads Basics

Kubernetes fundamental notions and basics of workloads for data-scientists and machine learning engineers.

Datadog vs. New Relic for ML platform observability

Compare Datadog and New Relic for Kubernetes-based ML workloads using telemetry coverage, workflow context, investigation speed, governance, and cost.

Experiment tracking in machine learning

Building machine learning models is an experimental process that requires several iterations. In this blog post we go over how Polyaxon manages experiment tracking.

What is a metadata store for machine learning

A metadata store is a central repository for storing all data generated in the process of building machine learning models. This data includes dataset versions, model versions, model parameters, model evaluation metrics, CPU and GPU utilization, just to mention a few.

StatefulSet vs. Deployment: differences and use cases

Compare Kubernetes StatefulSets and Deployments, including identity, storage, scaling, rollout behavior, and common use cases.

Polyaxon's new MLOps sandbox feature

At Polyaxon, we're always looking for ways to push the boundaries of what's possible with machine learning infrastructure, this is why we are excited about the upcoming sandbox deployment capabilities.

Manage cloud infrastructure for ML platforms

Operate cloud infrastructure for ML with declarative provisioning, clear ownership, workload isolation, capacity policies, cost allocation, and recovery exercises.

How Polyaxon streamlines MLOps

At Polyaxon, we're always looking for ways to push the boundaries of what's possible with machine learning. Our MLOps platform makes it easy to manage the entire lifecycle of your machine learning models.

Kubernetes network policies: an actionable guide

Use Kubernetes network policies to control pod traffic, restrict lateral movement, and make cluster networking less permissive by default.

Polyaxon v1.18: Improved run control

The run control, i.e. cog icon, is now visible on all tabs to allow users to easily perform actions on a run

Polyaxon v1.18: New comparison features

Polyaxon UI has new features that allows users to quickly compare and filter their runs

Polyaxon v1.18: New Tensorboard component and initializer

You can now start Tensorboard using the experiment names instead of the experiment uuids

Plan GCP Persistent Disk for ML workloads

Choose, size, monitor, and retire Google Cloud Persistent Disk volumes for training, notebooks, caches, and stateful ML services.

Polyaxon v1.18: Improve CLI with rich interface

We recently worked on an effort to improve the visual experience our CLI

Polyaxon v1.18: Support for annotations and env vars in connections schema

You can now define annotations and environment variables in your connections' schema

Polyaxon v1.18: Improved query language

Users can now filter by connections and artifacts

Polyaxon v1.18: Admin command to force clean operations

Admin users with access to kubectl can now force kill operations in a namespace

Polyaxon v1.17: Pulling & Pushing runs

Users can now package and pull runs to local paths or push runs from local paths to a Polyaxon deployment

Polyaxon v1.16: Inspecting chart data

Using the line chart widget you can now inspect the underlying data as an interactive table.

Polyaxon v1.16: Transferring runs, models, components, & artifacts

Users can now transfer runs, models, components, & artifacts from one project to another to reorganize their workspaces