AWS Architecture Blog

Category: Amazon SageMaker

Field Notes: Build a Cross-Validation Machine Learning Model Pipeline at Scale with Amazon SageMaker

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 […]

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Figure 1. OR optimization options

Emerging Solutions for Operations Research on AWS

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Operations research (OR) uses mathematical and analytical tools to arrive at optimal solutions for complex business problems like workforce scheduling. The mathematical techniques used to solve these problems, such as linear programming and mixed-integer programming, require the use of optimization […]

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Figure 2. Building Lake House architectures with AWS Glue

How to Accelerate Building a Lake House Architecture with AWS Glue

Customers are building databases, data warehouses, and data lake solutions in isolation from each other, each having its own separate data ingestion, storage, management, and governance layers. Often these disjointed efforts to build separate data stores end up creating data silos, data integration complexities, excessive data movement, and data consistency issues. These issues are preventing […]

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Figure 1. Data pipeline architecture using AWS Services

Building a Data Pipeline for Tracking Sporting Events Using AWS Services

In an evolving world that is increasingly connected, data-centric, and fast-paced, the sports industry is no exception. Amazon Web Services (AWS) has been helping customers in the sports industry gain real-time insights through analytics. You can re-invent and reimagine the fan experience by tracking sports actions and activities. In this blog post, we will highlight […]

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Fraud prevention sample architecture

Preventing Free Trial Abuse with AWS Managed Services

Free trial promotions are a popular marketing tactic, but they can also be a common source of fraud for ecommerce retailers. So, how do you identify fraudulent users? And what are some effective ways to prevent free trial abuse? This blog post outlines common free trial abuse attack vectors and presents prevention techniques. We’ll show […]

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Figure 1. Notional architecture for improving forecasting accuracy solution and SAP integration

Improving Retail Forecast Accuracy with Machine Learning

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 […]

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How to redact confidential information in your ML pipeline

Integrating Redaction of FinServ Data into a Machine Learning Pipeline

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 […]

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Figure 1 - Architecture overview of the solution to launch a fully configured AWS Deep Learning Desktop with NICE DCV

Field Notes: Launch a Fully Configured AWS Deep Learning Desktop with NICE DCV

You want to start quickly when doing deep learning using GPU-activated Elastic Compute Cloud (Amazon EC2) instances in the AWS Cloud. Although AWS provides end-to-end machine learning (ML) in Amazon SageMaker, working at the deep learning frameworks level, the quickest way to start is with AWS Deep Learning AMIs (DLAMIs), which provide preconfigured Conda environments for […]

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Figure 2: AI Factory high-level architecture

ERGO Breaks New Frontiers for Insurance with AI Factory on AWS

This post is co-authored with Piotr Klesta, Robert Meisner and Lukasz Luszczynski of ERGO Artificial intelligence (AI) and related technologies are already finding applications in our homes, cars, industries, and offices. The insurance business is no exception to this. When AI is implemented correctly, it adds a major competitive advantage. It enhances the decision-making process, […]

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Figure 1. Data pipeline that cleans, processes, and segments data

How Financial Institutions can use AWS to Address Regulatory Reporting

Since the 2008 financial crisis, banking supervisory institutions such as the Basel Committee on Banking Supervision (BCBS) have strengthened regulations. There is now increased oversight over the financial services industry. For banks, making the necessary changes to comply with these rules is a challenging, multi-year effort. Basel IV, a massive update to existing rules, is […]

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