Favorite Described in a Harvard Business School article from 2016, here is the pledge that WD40 staff take to commit to organisational-wide sharing and learning. This is the Learning Manic Pledge; part of WD40’s “tribal culture“. The pledge is as follows: I am responsible for taking action, asking questions, getting
Favorite With AI systems being so complex, concepts like “program” or “source code” in the Open Source Definition are challenged in new and surprising ways. The post Welcome to Deep Dive AI first appeared on Voices of Open Source. Click Here to View Original Source (opensource.org)
Favorite This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with
Favorite We spent this past weekend in Los Angeles at the SCaLE 19X conference and it… The post Reflecting on SCaLE 19x first appeared on Voices of Open Source. Click Here to View Original Source (opensource.org)
Favorite Posted by Bingyi Cao, Software Engineer, Google Research, and Mário Lipovský, Software Engineer, Google Lens Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction. One challenging type of computer vision problem is instance-level recognition (ILR) — given
Favorite Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working
Favorite Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line
Favorite Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual
Favorite Posted by Arun Kandoor, Software Engineer, Google Research The increasing demand for machine learning (ML) model inference on-device (for mobile devices, tablets, etc.) is driven by the rise of compute-intensive applications, the need to keep certain data on device for privacy and security reasons, and the desire to provide
Favorite There are five common stages of the OSPOs that identify the status of your organization’s involvement in Open Source: use it as suggestions to advance your Open Source journey. The post The five stages of the Open Source Program Office first appeared on Voices of Open Source. Click Here