AWS Architecture Blog
Category: Amazon Machine Learning
Automating Recommendation Engine Training with Amazon Personalize and AWS Glue
Customers from startups to enterprises observe increased revenue when personalizing customer interactions. Still, many companies are not yet leveraging the power of personalization, or, are relying solely on rule-based strategies. Those strategies are effort-intensive to maintain and not effective. Common reasons for not launching machine learning (ML) based personalization projects include: the complexity of aggregating […]
Field Notes: Comparing Algorithm Performance Using MLOps and the AWS Cloud Development Kit
Comparing machine learning algorithm performance is fundamental for machine learning practitioners, and data scientists. The goal is to evaluate the appropriate algorithm to implement for a known business problem. Machine learning performance is often correlated to the usefulness of the model deployed. Improving the performance of the model typically results in an increased accuracy of […]
AWS Architecture Monthly Magazine: Robotics
September’s issue of AWS Architecture Monthly issue is all about robotics. Discover why iRobot, the creator of your favorite (though maybe not your pet’s favorite) little robot vacuum, decided to move its mission-critical platform to the serverless architecture of AWS. Learn how and why you sometimes need to test in a virtual environment instead of […]
AWS Architecture Monthly Magazine: Agriculture
In this month’s issue of AWS Architecture Monthly, Worldwide Tech Lead for Agriculture, Karen Hildebrand (who’s also a fourth generation farmer) refers to agriculture as “the connective tissue our world needs to survive.” As our expert for August’s Agriculture issue, she also talks about what role cloud will play in future development efforts in this […]
Field Notes: Inference C++ Models Using SageMaker Processing
Machine learning has existed for decades. Before the prevalence of doing machine learning with Python, many other languages such as Java, and C++ were used to build models. Refactoring legacy models in C++ or Java could be forbiddingly expensive and time consuming. Customers need to know how they can bring their legacy models in C++ […]
Field Notes: Bring your C#.NET skills to Amazon SageMaker
Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the undifferentiated heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. Amazon SageMaker Notebooks are one-click Jupyter Notebooks […]
Architecture Monthly Magazine for July: Machine Learning
Every month, AWS publishes the AWS Architecture Monthly Magazine (available for free on Kindle and Flipboard) that curates some of the best technical and video content from around AWS. In the June edition, we offered several pieces of content related to Internet of Things (IoT). This month we’re talking about artificial intelligence (AI), namely machine […]