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Favorite In the agriculture sector, the problem of identifying and counting the amount of fruit on trees plays an important role in crop estimation. The concept of renting and leasing a tree is becoming popular, where a tree owner leases the tree every year before the harvest based on the
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Shared by AWS Machine Learning August 11, 2022
Favorite In November 2021, in collaboration with RStudio PBC, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench IDE in the cloud. You can now bring your current RStudio license to easily migrate your self-managed RStudio environments to Amazon SageMaker in just
Read More Shared by AWS Machine Learning August 10, 2022
Favorite Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning
Read More Shared by AWS Machine Learning August 9, 2022
Favorite This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with
Read More Shared by AWS Machine Learning August 5, 2022
Favorite Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working
Read More Shared by AWS Machine Learning August 4, 2022
Favorite Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line
Read More Shared by AWS Machine Learning August 4, 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
Read More 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
Read More Shared by AWS Machine Learning August 2, 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
Read More 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
Read More Shared by AWS Machine Learning July 30, 2022