Favorite Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when
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Shared by AWS Machine Learning June 8, 2022
Favorite In today’s digital landscape, customers are expecting a high-quality experience that is responsive and delightful. Chatbots and virtual assistants have transformed the customer experience from a point-and-click or a drag-and-drop experience to one that is driven by voice or text. You can create a more engaging experience by further
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Shared by AWS Machine Learning June 8, 2022
Favorite Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio – a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks
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Shared by AWS Machine Learning June 7, 2022
Favorite This is a guest post co-written by Juan Francisco Fernandez, ML Engineer in Adevinta Spain, and AWS AI/ML Specialist Solutions Architects Antonio Rodriguez and João Moura. InfoJobs, a subsidiary company of the Adevinta group, provides the perfect match between candidates looking for their next job position and employers looking
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Shared by AWS Machine Learning June 7, 2022
Favorite In this post, we talk about how to split a machine learning (ML) dataset into train, test, and validation datasets with Amazon SageMaker Data Wrangler so you can easily split your datasets with minimal to no code. Data used for ML is typically split into the following datasets: Training
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Shared by AWS Machine Learning June 7, 2022
Favorite Posted by Paul Hongsuck Seo and Arsha Nagrani, Research Scientists, Google Research, Perception Team Multimodal video captioning systems utilize both the video frames and speech to generate natural language descriptions (captions) of videos. Such systems are stepping stones towards the longstanding goal of building multimodal conversational systems that effortlessly
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Shared by Google AI Technology June 7, 2022
Favorite Retail businesses are data-driven—they analyze data to get insights about consumer behavior, understand shopping trends, make product recommendations, optimize websites, plan for inventory, and forecast sales. A common approach for sales forecasting is to use historical sales data to predict future demand. Forecasting future demand is critical for planning
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Shared by AWS Machine Learning June 6, 2022
Favorite People are often instinctively drawn towards one component of Knowledge Management. Here’s a way of looking at those components. Choices about the approach to KM are often made implicitly, emotionally, or through assumption, so it’s worth taking time out to analyse these approaches intellectually, before starting work on your
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Shared by Nick Milton June 6, 2022
Favorite Today, we’re excited to announce that Amazon SageMaker now supports the ability to configure Instance Metadata Service Version 2 (IMDSv2) for Notebook Instances, and for administrators to control the minimum version with which end-users create new Notebook Instances. You can now choose IMDSv2 only for your new and existing
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Shared by AWS Machine Learning June 3, 2022
Favorite You may have applications that generate streaming data that is full of records containing customer case notes, product reviews, and social media messages, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing positive or negative sentiment, translate their comments
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Shared by AWS Machine Learning June 3, 2022
Shared by AWS Machine Learning June 8, 2022
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