January 28, 2020

228 words 2 mins read

Jumpstarting an OSPO and How to Measure Internal Community Success

Jumpstarting an OSPO and How to Measure Internal Community Success

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 …
Talk Title Jumpstarting an OSPO and How to Measure Internal Community Success
Speakers Alison Yu (Open Source Community Manager, Indeed)
Conference Open Source Summit + ELC Europe
Conf Tag
Location Lyon, France
Date Oct 27-Nov 1, 2019
URL Talk Page
Slides Talk Slides
Video

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 success.In this talk, Alison will go over how we got executive and marketing buy-in, and the different initiatives that are targeted to our internal audience.Now the mighty question: What metrics should we be tracking?You may be asking yourself, how do I get a sense of the community’s activity and growth? Alison will go over what data that we collect, why this specific data, and how we use the data to show success, update community strategy, point out weaknesses, and eliminate obstacles as they arise.This talk will be an expanded version of Alison’s DevRelCon SF talk. Alison will go into more detail around measuring metrics and how we implement changes due to the metrics.

metrics tracking open source
comments powered by Disqus
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.
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.
Metrics that Matter: Forging a Path to More Diverse, Inclusive Communities

Metrics that Matter: Forging a Path to More Diverse, Inclusive Communities

January 23, 2020

While it is well recognized that diversity and inclusion are central to the health of open source communities, numbers lag and the ability to foster inclusive environments remains challenging. The CHA …
Distributed tracing and monitoring with OpenTelemetry

Distributed tracing and monitoring with OpenTelemetry

January 16, 2020

OpenCensus is a new standard for tracing and metrics of cloud services, used for observability into applications that span multiple clouds and technological stacks. Simon Zeltser explains how to use vendor-agnostic client libraries for OpenCensus to export telemetry to common distributed tracing systems and covers core concepts like tags, metrics, exporters, zPages, and context propagation.
Bridging the Gap Between Open Source and University Students and Women

Bridging the Gap Between Open Source and University Students and Women

January 12, 2020

There exists a large number of programs that act as a source of motivation for university students to contribute to open source such as Google Summer of Code, Outreachy, Rails girls summer of code etc …
Mutant tests too: The SQL

Mutant tests too: The SQL

January 8, 2020

Elliot West and Jay Green share approaches for applying software engineering best practices to SQL-based data applications to improve maintainability and data quality. Using open source tools, Elliot and Jay show how to build effective test suites for Apache Hive code bases and offer an overview of Mutant Swarm, a tool to identify weaknesses in tests and to measure SQL code coverage.