The default Kubernetes scheduler does a fantastic job for typical workloads, but when you have specific requirements (like higher level application metrics) you might need other scheduling methods. …

A good practice when deploying applications in Kubernetes is to set proper instrumentation to gather insights and solve general monitoring needs. Logging is a fundamental piece of the instrumentation …

Deploying machine learning models and deep learning models in production is hard. Harish Doddi and Jerry Xu outline the enterprise data science lifecycle, covering how production model deployment flow works, challenges, best practices, and lessons learned. Along the way, they explain why monitoring models in the production should be mandatory.

Most machine learning algorithms are designed to work on stationary data, but real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Emre Velipasaoglu evaluates monitoring methods for applicability in modern fast data and streaming applications.

Observability and monitoring are different, but complementary, needs for production applications. While monitoring focuses on measuring the overall health of your systems, observability aims to provid …

Kubernetes generates a wealth of metrics. Some explicitly within the Kubernetes API server, the Kublet, and cAdvisor or implicitly by observing events such as the kube-state-metrics project. A subset …

Have you ever thought what are the sources of useful signals in Kubernetes? How to collect those data? The data that are crucial for understanding the system. How to ingest them to a human friendly to …

As serverless applications grow more complex, function composition, or the ability for functions to call each other, becomes important. This talk will dive into three different approaches to compose …

LinkedIn runs more than 1,800+ Kafka brokers that deliver more than two trillion messages a day. Running Kafka at such a scale makes automated operations a necessity. Jiangjie Qin shares lessons learned from operating Kafka at scale with minimum human intervention.

Anomaly detection is white hot in the monitoring industry, but many don't really understand or care about it, while others repeat the same pattern many times. Why? And what can we do about it? Baron Schwartz explains how he arrived at a "post-anomaly detection" point of view.

In Media Build and Products under Oath, We run 12 production Kubernetes clusters running across our data centers with ~1200 machines with multi-tenant deployments. We monitor our cluster with Promethe …

With telemetry service provided by Istio, containerized application performance monitoring has never been easier. But what do you do with slow response time when you have application deploying at scal …

Loodse and CoreOS run many different setups across multiple heterogeneous public and private clouds. In this context, they faced some challenges with deploying the monitoring infrastructure. Matthias …

This talk will start by describing the need for making Kubernetes aware of resources like GPUs. Then it will briefly go into the history of GPU support in Kubernetes and the various backwards incompat …

Smart contract performance and debuggability are very important criteria to consider when building mission critical enterprise and public blockchains. In their talk, Vijayendra Bhamidipati and Michael …

Cloud deployments provide many benefits for developers and operations pre-configured compute and other resources, scaling on request, managed monitoring and operations, and consumption-based pricing …