Tag: Amazon SageMaker

Recommender systems help you tailor customer experiences on online platforms. Amazon Personalize is an artificial intelligence and machine learning service that specializes in developing recommender system solutions. It automatically examines the data, performs feature and algorithm selection, optimizes the model based on your data, and deploys and hosts the model for real-time recommendation inference. However, […]

FORMULA 1 (F1) turns 70 years old in 2020 and is one of the few sports that combines real-time skill with engineering and technical prowess. Technology has always played a central role in F1; where the evolution of the rules and tools is built into the DNA of F1. This keeps fans engaged and drivers […]

Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that […]

July 2022: Post was reviewed for accuracy. Amazon SageMaker enables organizations to build, train, and deploy machine learning models. Consumer-facing organizations can use it to enrich their customers’ experiences, for example, by making personalized product recommendations, or by automatically tailoring application behavior based on customers’ observed preferences. When building such applications, one key architectural consideration […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning (RL) models using Amazon SageMaker RL. […]

Amazon SageMaker Operators for Kubernetes allows you to augment your existing Kubernetes cluster with SageMaker hosted endpoints. Machine learning inferencing requires investment to create a reliable and efficient service. For an XGBoost model, developers have to create an application, such as through Flask that will load the model and then run the endpoint, which requires […]

You have many choices for building, training, and deploying machine learning (ML) models. Weighing the financial considerations of different cloud solutions requires detailed analysis. You must consider the infrastructure, operational, and security costs for each step of the ML workflow, as well as the size and expertise of your data science teams. The Total Cost […]

The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually—3% of the nation’s $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses […]

This is a guest post from Millennium Management. In their own words, “Millennium Management is a global investment management firm, established in 1989, with over 2,900 employees and $39.2 billion in assets under management as of August 2, 2019.” Millennium Management is comprised of a large number of specialized trading teams across the United States, […]

Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute intensive, keeping compute costs low for […]