Favorite With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications

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Shared by AWS Machine Learning July 24, 2021

Favorite This is a guest post authored by Paul Ngo, US Gas Technical and Operational Services Data Team Lead at TC Energy. TC Energy operates a network of pipelines, including 57,900 miles of natural gas and 3,000 miles of oil and liquid pipelines, throughout North America. TC Energy enables a

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Shared by AWS Machine Learning July 23, 2021

Favorite For the past few decades, physician burnout has been a challenge in the healthcare industry. Although patient interaction and diagnosis are critical aspects of a physician’s job, administrative tasks are equally taxing and time-consuming. Physicians and clinicians must keep a detailed medical record for each patient. That record is

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Shared by AWS Machine Learning July 23, 2021

Favorite Every machine learning (ML) model demands data to train it. If your model isn’t predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process—second only to data cleaning. What data cleaning looks like varies from dataset to

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Shared by AWS Machine Learning July 23, 2021

Favorite Cities across the world are transforming their public services infrastructure with the mission of enhancing the quality of life of its residents. Roads and traffic management systems are part of the central nervous system of every city. They need intelligent monitoring and automation in order to prevent substantial productivity

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Shared by AWS Machine Learning July 22, 2021

Favorite For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human

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Shared by AWS Machine Learning July 22, 2021

Favorite Have you noticed that your shopping preferences are influenced by the weather? For example, on hot days would you rather drink a lemonade vs. a hot coffee? Customers from consumer-packaged goods (CPG) and retail industries wanted to better understand how weather conditions like temperature and rain can be used

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Shared by AWS Machine Learning July 21, 2021

Favorite You might use traditional methods to forecast future business outcomes, but these traditional methods are often not flexible enough to account for varying factors, such as weather or promotions, outside of the traditional time series data considered. With the advancement of machine learning (ML) and the elasticity that the

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Shared by AWS Machine Learning July 21, 2021

Favorite Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their

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Shared by AWS Machine Learning July 21, 2021

Favorite Amazon Personalize now enables you to tap into the information trapped in product descriptions, product reviews, movie synopses, or other unstructured text and use it when generating personalized recommendations. Product descriptions provide important information about the features and benefits of products. Amazon Personalize can use the investments made to

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Shared by AWS Machine Learning July 20, 2021