Category: Amazon Machine Learning

When building a machine learning algorithm, such as a regression or classification algorithm, a common goal is to produce a generalized model. This is so that it performs well on new data that the model has not seen before. Overfitting and underfitting are two fundamental causes of poor performance for machine learning models. A model […]

As part of AWS Professional Services, we work with customers across different industries to understand their needs and supplement their teams with specialized skills and experience. Some of our customers are internal teams from the Amazon retail organization who request our help with their initiatives. One of these teams, the Global Environmental Affairs team, identifies […]

The global retail market continues to grow larger and the influx of consumer data increases daily. The rise in volume, variety, and velocity of data poses challenges with demand forecasting and inventory planning. Outdated systems generate inaccurate demand forecasts. This results in multiple challenges for retailers. They are faced with over-stocking and lost sales, and […]

Financial companies process hundreds of thousands of documents every day. These include loan and mortgage statements that contain large amounts of confidential customer information. Data privacy requires that sensitive data be redacted to protect the customer and the institution. Redacting digital and physical documents is time-consuming and labor-intensive. The accidental or inadvertent release of personal information […]

This post was co-written by Amr Ragab, AWS Sr. Solutions Architect, EC2 Engineering and Anant Nawalgaria, former AWS Professional Services EMEA. One of the most common needs we have heard from customers in Autonomous Vehicle (AV) development, is to launch a hybrid deployment environment at scale. As vehicle fleets are deployed across the globe, they […]

Recently, we have had customers express the desire to build “omni-channels.” These omni-channels provide a centralized overview of digital engagement channels that help you better understand your customers and offer a more personalized experience. Many companies have tried or are trying to implement an omni-channel strategy. However, because most existing channels are built on different platforms and […]

The goal of the AWS Architecture Blog is to highlight best practices and provide architectural guidance. We publish thought leadership pieces that encourage readers to discover other technical documentation, such as solutions and managed solutions, other AWS blogs, videos, reference architectures, whitepapers, and guides, Training & Certification, case studies, and the AWS Architecture Monthly Magazine. […]

This post was co-written by Abdullah Sahin, senior technology architect at Accenture, and Muhammad Qasim, software engineer at Accenture.  Organizations deploying call-center chat bots are interested in evolving their solutions continuously, in response to changing customer demands. When developing a smart chat bot, some requests can be predicted (for example following a new product launch […]

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 […]

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 […]