Kubeflow is the open-source machine learning toolkit on top of Kubernetes. Kubeflow translates steps in your data science workflow into Kubernetes jobs, providing the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks. Read more about Kubeflow

Welcome to another monthly update on what’s new from Sysdig! Happy 4th of July to our American audience, and bonne Bastille to our French friends. It’s been heating up in the northern hemisphere, so we hope you’ve all been managing to stay cool and safe. Our team continues to work hard to bring great new features to all of our customers, automatically and for free! The big news this month is our intent to acquire Apolicy, which has everyone full of excitement.

The challenges involved in deploying and managing microservices have led to the creation of the service mesh, a tool for adding observability, security, and traffic management capabilities at the application layer. While a service mesh is intended to help developers and SREs with a number of use cases related to service-to-service communication within Kubernetes clusters, a service mesh also adds operational complexity and introduces an additional control plane for security teams to manage.

Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.