AWS Machine Learning Blog

Category: Analytics

Perform interactive data processing using Spark in Amazon SageMaker Studio Notebooks

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. You can now use Studio notebooks to securely connect to Amazon EMR clusters and prepare vast amounts of data for […]

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You already know how to use Amazon Athena to transform data in Amazon S3 using simple SQL commands

Translate, redact, and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend

Update April 5th 2021: Post updated per Amazon Athena UDF SQL syntax updates. You have Amazon Simple Storage Service (Amazon S3) buckets full of files containing incoming customer chats, product reviews, and social media feeds, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy […]

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The following diagram illustrates our solution architecture.

Setting up Amazon Personalize with AWS Glue

Data can be used in a variety of ways to satisfy the needs of different business units, such as marketing, sales, or product. In this post, we focus on using data to create personalized recommendations to improve end-user engagement. Most ecommerce applications consume a huge amount of customer data that can be used to provide […]

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The following is the architecture diagram for integrating online ML inference in a telemedicine contact flow via Amazon Connect.

Applying voice classification in an Amazon Connect telemedicine contact flow

Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary ML classifier can classify healthy and […]

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The following diagram shows the serverless architecture that you build.

Setting up an IVR to collect customer feedback via phone using Amazon Connect and AWS AI Services

As many companies place their focus on customer centricity, customer feedback becomes a top priority. However, as new laws are formed, for instance GDPR in Europe, collecting feedback from customers can become increasingly difficult. One means of collecting this feedback is via phone. When a customer calls an agency or call center, feedback may be […]

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Forecasting AWS spend using the AWS Cost and Usage Reports, AWS Glue DataBrew, and Amazon Forecast

AWS Cost Explorer enables you to view and analyze your AWS Cost and Usage Reports (AWS CUR). You can also predict your overall cost associated with AWS services in the future by creating a forecast of AWS Cost Explorer, but you can’t view historical data beyond 12 months. Moreover, running custom machine learning (ML) models […]

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Saving time with personalized videos using AWS machine learning

CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually […]

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For an existing data lake registered with Lake Formation, the following diagram illustrates the proposed implementation.

Control and audit data exploration activities with Amazon SageMaker Studio and AWS Lake Formation

Certain industries are required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post walks you through the steps to implement access control and auditing capabilities on a per-user basis, using Amazon SageMaker Studio notebooks and AWS […]

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Automated model refresh with streaming data

In today’s world, being able to quickly bring on-premises machine learning (ML) models to the cloud is an integral part of any cloud migration journey. This post provides a step-by-step guide for launching a solution that facilitates the migration journey for large-scale ML workflows. This solution was developed by the Amazon ML Solutions Lab for […]

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Real-time anomaly detection for Amazon Connect call quality using Amazon ES

If your contact center is serving calls over the internet, network metrics like packet loss, jitter, and round-trip time are key to understanding call quality. In the post Easily monitor call quality with Amazon Connect, we introduced a solution that captures real-time metrics from the Amazon Connect softphone, stores them in Amazon Elasticsearch Service (Amazon […]

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