AWS for Industries

Tag: AWS Machine Learning

Oil Pump

Improving Safety and Logistics at Well Pads with Amazon Machine Learning Services

Introduction In remote upstream oil and gas facilities, such as well pads, energy companies frequently have various service contractors bringing in items, performing services, and removing items from the site. These facilities most often do not have permanent staff on location. It can be challenging for operators to know who is accessing the facilities. For […]

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How to Run What-if Scenarios for Trading Strategies with Amazon FinSpace

Introduction In an earlier blog post, we described an architecture for backtesting machine learning-based trading strategies on AWS. One of the key components in this architecture is the data management and analytics component. Depending on the specific use case, there are various options to implement this. Many companies adopt solutions based on data lakes and […]

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Predicting all-cause patient readmission risk using AWS data lake and machine learning

It’s no secret that hospital readmissions impact patient outcomes and the financial health of healthcare providers globally. Specifically in the United States, the Agency for Healthcare Research and Quality (AHRQ) shows that readmissions are some of the costliest episodes to treat, with costs reaching in excess of $41.3B. Hospitals and healthcare providers are looking for […]

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How utility executives elevate the customer experience with Amazon Connect

Utility executives share common benchmark success metrics when reporting to stakeholders including customers, regulators, elected officials, and investors. Regulated investor-owned utilities focus on long-term metrics such regulated rates of return (ROR) and service reliability benchmarks such as Customer Average Interruption Duration Index (CAIDI). Energy retail executives focus closely on customer satisfaction (CSAT) scores and customer […]

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Forecasting energy usage using Amazon machine learning and data lakes

Executives within utilities and energy providers of all types and sizes have multiple ongoing needs to forecast energy usage. For example, as chief customer officer, your teams can use energy forecasts at the household level to proactively engage homeowners with high bill alerts and predict pre-pay or month-end energy charges. As the head of energy […]

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