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Favorite In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values
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Shared by AWS Machine Learning August 12, 2021
Favorite This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica. Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental differences. Cortica was founded to fix the fragmented journey families typically navigate while seeking diagnoses and
Read More Shared by AWS Machine Learning August 11, 2021
Favorite As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount
Read More Shared by AWS Machine Learning August 11, 2021
Favorite Amazon Comprehend is a natural language processing (NLP) service that provides APIs to extract key phrases, contextual entities, events, sentiment from unstructured text, and more. Entities refer to things in your document such as people, places, organizations, credit card numbers, and so on. But what if you want to
Read More Shared by AWS Machine Learning August 10, 2021
Favorite This post was cowritten by Ziv Pollak, Machine Learning Team Lead, and Alex Thoreux, Web Analyst at Clearly. A pioneer in online shopping, Clearly launched their first site in 2000. Since then, they’ve grown to become one of the biggest online eyewear retailers in the world, providing customers across
Read More Shared by AWS Machine Learning August 10, 2021
Favorite Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations—no ML expertise required. Amazon Personalize makes it easy for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product
Read More Shared by AWS Machine Learning August 10, 2021
Favorite Accenture is known for pioneering innovative solutions to achieve customer success by using artificial intelligence (AI) and machine learning (ML) powered solutions with AWS services. To keep teams updated with latest ML services, Accenture seeks to gamify hands-on learning. One such event, AWS DeepComposer Battle of the Bands, hosted
Read More Shared by AWS Machine Learning August 7, 2021
Favorite Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics
Read More Shared by AWS Machine Learning August 7, 2021
Favorite The demand for audio and video media content is growing at an unprecedented rate. Organizations are using media to engage with their audiences like never before. Product documentation is increasingly published in video form, and podcasts are increasingly produced in place of blog posts. The recent explosion in the
Read More Shared by AWS Machine Learning August 7, 2021
Favorite Amazon Kendra is a fully managed, intelligent search service powered by machine learning. Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they’re looking for. Using keyword or natural language queries, employees and customers can find the right
Read More Shared by AWS Machine Learning August 6, 2021