Reflecting on SCaLE 19x
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)
Introducing the Google Universal Image Embedding Challenge
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
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Shared by Google AI Technology August 4, 2022
Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability
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
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Shared by AWS Machine Learning August 4, 2022
Amazon Comprehend announces lower annotation limits for custom entity recognition
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
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Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search
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
Read More Shared by Google AI Technology August 3, 2022
Efficient Sequence Modeling for On-Device ML
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
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The five stages of the Open Source Program Office
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
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Shared by voicesofopensource August 3, 2022
Scale YOLOv5 inference with Amazon SageMaker endpoints and AWS Lambda
Favorite After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon
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Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store
Favorite Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which
Read More Shared by AWS Machine Learning August 2, 2022
The value of pull-based community meetings
Favorite Don’t just run your community meetings as presentations; instead engage in real multi-way dialogue around important questions. Brown bag lunch, by Gloria, on Flickr I have blogged several times about Push and Pull in Knowledge Management (aka Knowledge Supply and demand); about the dangers of focusing only on Push (such a
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Shared by Nick Milton August 1, 2022