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Improving Content Moderation with Amazon Rekognition Bulk Analysis and Custom Moderation | Amazon Web Services
Amazon Rekognition makes it easy to add image and video analysis to your applications. It’s based on the same proven, highly scalable, deep learning...
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Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2 | Amazon Web Services
In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at...
Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning | Amazon Web Services
In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model...
How to use Whatsapp API to Send Messages
IntroductionIn today's digital age, WhatsApp has emerged as more than just a messaging app; it's a pivotal communication tool for businesses worldwide. With over...
Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services | Amazon Web Services
In the last few years Large Language Models (LLMs) have risen to prominence as outstanding tools capable of understanding, generating and manipulating text with...
Optimizing costs for Amazon SageMaker Canvas with automatic shutdown of idle apps | Amazon Web Services
Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more...
LangChain: A Complete Guide & Tutorial
At its core, LangChain is an innovative framework tailored for crafting applications that leverage the capabilities of language models. It's a toolkit designed for...
Detection and high-frequency monitoring of methane emission point sources using Amazon SageMaker geospatial capabilities | Amazon Web Services
Methane (CH4) is a major anthropogenic greenhouse gas that‘s a by-product of oil and gas extraction, coal mining, large-scale animal farming, and waste disposal,...
From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker | Amazon Web Services
This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Founded in 2011, Talent.com is one...
Prepare your data for Amazon Personalize with Amazon SageMaker Data Wrangler | Amazon Web Services
A recommendation engine is only as good as the data used to prepare it. Transforming raw data into a format that is suitable for...
Code Llama code generation models from Meta are now available via Amazon SageMaker JumpStart | Amazon Web Services
Today, we are excited to announce Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy with...
Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities | Amazon Web Services
This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. The analysis that...
Build and deploy ML inference applications from scratch using Amazon SageMaker | Amazon Web Services
As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business...
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