Favorite Each year, organizations lose tens of billions of dollars to online fraud globally. Organizations such as ecommerce companies and credit card companies use machine learning (ML) to detect online fraud. Some of the most common types of online fraud include email account compromise (personal or business), new account fraud,

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Shared by AWS Machine Learning February 19, 2021

Favorite As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on

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Shared by AWS Machine Learning February 18, 2021

Favorite Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary

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Shared by AWS Machine Learning February 18, 2021

Favorite In this post, we discuss solving numerical optimization problems using the very flexible Amazon SageMaker Processing API. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. This pattern is relevant to solving business-critical problems such as scheduling,

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Shared by AWS Machine Learning February 17, 2021

Favorite JavaScript Object Notation (JSON) is a schema-less, lightweight format for storing and transporting data. It’s a text-based, self-describing representation of structured data that is based on key-value pairs. JSON is supported either natively or through libraries in most major programming languages, and is commonly used to exchange information between

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Shared by AWS Machine Learning February 16, 2021

Favorite Training an accurate machine learning (ML) model requires many different steps, but none are potentially more important than data processing. Examples of processing steps include converting data to the input format expected by the ML algorithm, rescaling and normalizing, cleaning and tokenizing text, and many more. However, data processing

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Shared by AWS Machine Learning February 16, 2021

Favorite For many students, embarking on a higher education journey is an exciting time filled with new experiences. However, like anything new, it also can also bring plenty of questions to answer and obstacles to overcome. Oklahoma State University, Oklahoma City (OSU-OKC) recognized this, and was intent on providing a

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Shared by AWS Machine Learning February 16, 2021

Favorite PyTorch is an open-source machine learning (ML) library widely used to develop neural networks and ML models. Those models are usually trained on multiple GPU instances to speed up training, resulting in expensive training time and model sizes up to a few gigabytes. After they’re trained, these models are

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Shared by AWS Machine Learning February 12, 2021

Favorite The ability to rapidly iterate and train machine learning (ML) models is key to deriving business value from ML workloads. Because ML models often have many tunable parameters (known as hyperparameters) that can influence the model’s ability to effectively learn, data scientists often use a technique known as hyperparameter

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Shared by AWS Machine Learning February 10, 2021

Favorite As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing

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Shared by AWS Machine Learning February 10, 2021