Artificial Intelligence

Category: Advanced (300)

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) […]

Deploy BLOOM-176B and OPT-30B on Amazon SageMaker with large model inference Deep Learning Containers and DeepSpeed

April 2023: This post was reviewed and updated for accuracy. The last few years have seen rapid development in the field of deep learning. Although hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly encounter issues deploying their large deep learning models […]

Improve data extraction and document processing with Amazon Textract

Intelligent document processing (IDP) has seen widespread adoption across enterprise and government organizations. Gartner estimates the IDP market will grow more than 100% year over year, and is projected to reach $4.8 billion in 2022. IDP helps transform structured, semi-structured, and unstructured data from a variety of document formats into actionable information. Processing unstructured data […]

Automated exploratory data analysis and model operationalization framework with a human in the loop

Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase […]

Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select the target column to predict, […]