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
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Shared by Google AI Technology August 3, 2022
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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Shared by Google AI Technology August 3, 2022
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
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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Shared by AWS Machine Learning August 3, 2022
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
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Shared by AWS Machine Learning August 2, 2022
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
Favorite Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large
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Shared by AWS Machine Learning July 30, 2022
Favorite This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward
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Shared by AWS Machine Learning July 30, 2022
Favorite Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic
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Shared by AWS Machine Learning July 30, 2022
Favorite There have been many recent advancements in the NLP domain. Pre-trained models and fully managed NLP services have democratised access and adoption of NLP. Amazon Comprehend is a fully managed service that can perform NLP tasks like custom entity recognition, topic modelling, sentiment analysis and more to extract insights from data
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Shared by AWS Machine Learning July 30, 2022
Shared by Google AI Technology August 3, 2022
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