metrics

Building Telemetry and Anomaly Detection Models for Cloud Native Storage

Building Telemetry and Anomaly Detection Models for Cloud Native Storage

March 4, 2020

Integrating with heterogeneous storage in the Cloud Native environment has always been a challenge. How to detect problems and fix them in a timely fashion is important for mission critical workloads. …
How to build an evolutionary architecture

How to build an evolutionary architecture

March 2, 2020

Around 2017, Antonio Jimenez and Pedro Martos embarked on an ambitious journey: to redefine one of the company's most mission-critical, most complex products from scratch. Join them as they explore how you can achieve an evolutionary architecture from solid foundations such as microservices architecture within a continuous delivery pipeline.
Bridging, not Breaking Tradition: Use Service Mesh Expansion to Connect Legacy Workloads to Kubernetes Services

Bridging, not Breaking Tradition: Use Service Mesh Expansion to Connect Legacy Workloads to Kubernetes Services

March 1, 2020

Kubernetes has become a preferred platform for hosting distributed and portable services and applications. With this, Istio service mesh can be deployed to address service discovery, service-to-servic …
Accelerating engineering delivery tempo

Accelerating engineering delivery tempo

February 29, 2020

The Splice engineering team grew almost 10 times in 18 months. The delivery practices that worked when it was 5 people broke way before it got to 50. Juan Pablo Buritica explains how the engineering team accelerated delivery using industry insights and data.
Istio on Knative Lessons Learned: How Istio is Fit for Serverless Platform

Istio on Knative Lessons Learned: How Istio is Fit for Serverless Platform

February 29, 2020

Serverless is the emerging technology to support microservices. Istio is a popular service mesh platform. How Istio is fit for a serverless platform? Its a question that developers and users of both …
Building an Open Autonomous Data Platform

Building an Open Autonomous Data Platform

February 27, 2020

OpenSDS is an open source project under Linux Foundation. It aims at addressing storage integration challenges in cloud native environments. Its vision is to connect siloed data solutions to build a s …
How to make sense of real user performance metrics

How to make sense of real user performance metrics

February 27, 2020

Gilles Dubuc takes a deep dive into how Wikipedia interprets large amounts of real user performance data and the many pitfalls you can fall into when doing so.
Keynote: Trusted AI: Bringing Trust Back into AI through Open Source

Keynote: Trusted AI: Bringing Trust Back into AI through Open Source

February 27, 2020

As businesses move beyond experimentation to full-blown AI projects across the enterprise, they are recognizing that theres more to successful implementations than simply having the right datasets, A …
M3 and Prometheus: Monitoring at planet scale for everyone

M3 and Prometheus: Monitoring at planet scale for everyone

February 27, 2020

Rob Skillington and ukasz Szczsny explore scaling monitoring, alerting, and configurational complexity for a single view of your applications, databases, infrastructure, and operations across all regions using M3 and Prometheus.
Putting a Microscope on Your Application Monitoring via ebpf

Putting a Microscope on Your Application Monitoring via ebpf

February 27, 2020

The problem with most of the monitoring systems and tools today is that they identify symptoms of the problem like a high cpu utilization but are unable to tell the cause. This leads to visible proble …
Metrics - When One Size Doesnt Fit All

Metrics - When One Size Doesnt Fit All

February 26, 2020

There are different ways to assess an open source projects success, but measurable metrics (like pull requests and contributions) typically play some role. One of the challenges Uber has faced involv …
Moving to a Service Mesh

Moving to a Service Mesh

February 25, 2020

Historically, Indeed has used Boxcar (Indeeds proprietary framework) to build distributed systems. Over the last year, we have been shifting several of our systems to use gRPC over anEnvoy service m …
Understanding Kubernetes

Understanding Kubernetes

February 25, 2020

Jonathan Johnson introduces you to Kubernetes for software engineers through concepts and a hands-on tutorials using KataCoda.com/javajon.
What remains of dashboards and metrics without the hype and anti-patterns

What remains of dashboards and metrics without the hype and anti-patterns

February 24, 2020

Open source tools for dashboarding and metrics have seen massive adoption in recent years. Riding the hype, the new, shiny tools are inevitably confronted with overblown expectations and problematic usage patterns, causing frustration and criticism. Bjrn Rabenstein outlines how to use dashboards and metrics effectively rather than condemning them altogether.
Open Source Tools for ML Experiments Management

Open Source Tools for ML Experiments Management

February 23, 2020

The rise of new AI and ML requires new workflows and new tools: data versioning, ML pipeline versioning, experiments metrics visualization and others that have not been formalized and even named yet.T …
Service Mush: Debugging Istio Deployments

Service Mush: Debugging Istio Deployments

February 20, 2020

So youve decided to deploy Istio to power-up your microservices. Now what do you do when things dont work as expected? How do you debug traffic management and canary rollouts and what if traffic isn …
Data science and the business of Major League Baseball

Data science and the business of Major League Baseball

February 16, 2020

Using SAS, Python, and AWS SageMaker, Major League Baseball's (MLB's) data science team outlines how it predicts ticket purchasers likelihood to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service.
Measuring and Improving the Efficiency of Software Delivery

Measuring and Improving the Efficiency of Software Delivery

February 16, 2020

Imagine you start work on a feature on Monday at 10 am, and it goes live on Thursday at 10 am. We call that difference Delivery Lead Time – in this case, a very respectable three days.When you decrea …
CHAOSS: Making Open Source Project Health Transparent and Actionable

CHAOSS: Making Open Source Project Health Transparent and Actionable

February 15, 2020

This presentation will focus on the work of the CHAOSS project during its first two years. The presentation will highlight efforts to-date from the project working groups as well as the open source so …
A Community-generated Content Strategy that Works

A Community-generated Content Strategy that Works

February 13, 2020

Go behind the scenes at Opensource.com to learn how we are building a community and creating the world's premier open source story telling platform. On average, we publish 90 articles, generate 1.5M p …
ThirdEye: LinkedIns business-wide monitoring platform

ThirdEye: LinkedIns business-wide monitoring platform

February 8, 2020

Failures or issues in a product or service can negatively affect the business. Detecting issues in advance and recovering from them is crucial to keeping the business alive. Join Akshay Rai to learn more about LinkedIn's next-generation open source monitoring platform, an integrated solution for real-time alerting and collaborative analysis.
Machine Learning Models and Datasets Versioning Practices and Tools

Machine Learning Models and Datasets Versioning Practices and Tools

February 5, 2020

The rise of AI and ML changes development workflow and requires new development tools: data versioning, ML pipeline versioning, experiments metrics tracking and others that have not been formalized an …
Talking to the machines: Monitoring production machine learning systems

Talking to the machines: Monitoring production machine learning systems

February 3, 2020

Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems.
A decentralized reference architecture for cloud native applications (sponsored by Ballerina)

A decentralized reference architecture for cloud native applications (sponsored by Ballerina)

February 1, 2020

Asanka Abeysinghe explores cell-based architecture, a self-contained composable unit of architecture. The cell is independently scalable. Its independently deployable. Its independently governed. It's part of an ecosystem of cells. A cell-based architecture is a common pattern that any enterprise can connect architecture, implementation, and deployment by making autonomous development teams.
Jumpstarting an OSPO and How to Measure Internal Community Success

Jumpstarting an OSPO and How to Measure Internal Community Success

January 28, 2020

Getting buy-in for an Open Source Program Office (OSPO) can be tough, for a successful program, you need almost the entire company to buy-in. And once you get buy-in? Be prepared to measure and prove …
Machine learning vital signs: Metrics and monitoring of AI in production

Machine learning vital signs: Metrics and monitoring of AI in production

January 27, 2020

Production artificial intelligence systems are interacting with the real world, and it's terrifying that oftentimes nobody has any idea how they're performing on live data. Donald Miner details why you should track your models in production over time, explains how you can implement proper logging and metrics for models, and details metrics you should probably be capturing.
Observability for data pipelines: Monitoring, alerting, and tracing lineage

Observability for data pipelines: Monitoring, alerting, and tracing lineage

January 27, 2020

Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain and iterate even after they are initially built and validated. Jiaqi Liu explores how to factor in monitoring, alerting, and tracing data lineage when building data applications that move and transform data across multiple dependencies.
Removing unfair bias in machine learning using open source (sponsored by IBM)

Removing unfair bias in machine learning using open source (sponsored by IBM)

January 25, 2020

ML models are increasingly used to make decisions that impact lives. Ana Echeverri and Trisha Mahoney walk you through how to use the open source Python package AI Fairness 360, developed by IBM researchers, a comprehensive open source toolkit empowering users with metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.