AWS Machine Learning Blog

Category: Advanced (300)

Real-time analysis of customer sentiment using AWS

Companies that sell products or services online need to constantly monitor customer reviews left on their website after purchasing a product. The company’s marketing and customer service departments analyze these reviews to understand customer sentiment. For example, marketing could use this data to create campaigns targeting different customer segments. Customer service departments could use this […]

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. These documents contain critical information that’s key to making important business decisions. As an organization grows, […]

Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes. Data underpins ML and AI use cases and is a strategic asset to an organization. As data is growing at an exponential rate, organizations are looking to set up an integrated, […]

Build a cross-account MLOps workflow using the Amazon SageMaker model registry

A well-designed CI/CD pipeline is essential to scale any software development workflow effectively. When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. At AWS, we’re continuing to innovate to simplify the MLOps workflow. In this post, we discuss some […]

Enabling hybrid ML workflows on Amazon EKS and Amazon SageMaker with one-click Kubeflow on AWS deployment

Today, many AWS customers are building enterprise-ready machine learning (ML) platforms on Amazon Elastic Kubernetes Service (Amazon EKS) using Kubeflow on AWS (an AWS-specific distribution of Kubeflow) across many use cases, including computer vision, natural language understanding, speech translation, and financial modeling. With the latest release of open-source Kubeflow v1.6.1, the Kubeflow community continues to […]

Malware detection and classification with Amazon Rekognition

According to an article by Cybersecurity Ventures, the damage caused by Ransomware (a type of malware that can block users from accessing their data unless they pay a ransom) increased by 57 times in 2021 as compared to 2015. Furthermore, it’s predicted to cost its victims $265 billion (USD) annually by 2031. At the time […]

Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

In machine learning (ML), data quality has direct impact on model quality. This is why data scientists and data engineers spend significant amount of time perfecting training datasets. Nevertheless, no dataset is perfect—there are trade-offs to the preprocessing techniques such as oversampling, normalization, and imputation. Also, mistakes and errors could creep in at various stages […]

How Prodege saved $1.5 million in annual human review costs using low-code computer vision AI

This post was co-authored by Arun Gupta, the Director of Business Intelligence at Prodege, LLC. Prodege is a data-driven marketing and consumer insights platform comprised of consumer brands—Swagbucks, MyPoints, Tada, ySense, InboxDollars, InboxPounds, DailyRewards, PollFish, and Upromise—along with a complementary suite of business solutions for marketers and researchers. Prodege has 120 million users and has […]

Model hosting patterns in Amazon SageMaker, Part 2: Getting started with deploying real time models on SageMaker

Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four Inference options: Real-Time Inference Serverless Inference Asynchronous Inference Batch Transform These four options can be broadly classified into Online […]

Predict lung cancer survival status using multimodal data on Amazon SageMaker JumpStart

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and is composed of tumors with significant molecular heterogeneity resulting from differences in intrinsic oncogenic signaling pathways [1]. Enabling precision medicine, anticipating patient preferences, detecting disease, and improving care quality for NSCLC patients are important topics among healthcare and life sciences (HCLS) […]