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Tag: Feature Engineering

Alida gains deeper understanding of customer feedback with Amazon Bedrock | Amazon Web Services

This post is co-written with Sherwin Chu from Alida. Alida helps the world’s biggest brands create highly...

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Identify cybersecurity anomalies in your Amazon Security Lake data using Amazon SageMaker | Amazon Web Services

Customers are faced with increasing security threats and vulnerabilities across infrastructure and application resources as their digital footprint has expanded and the business impact...

Streamlining ETL data processing 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. Established in 2011, Talent.com aggregates paid...

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions | Amazon Web Services

This is a guest post co-authored by Nafi Ahmet Turgut, Mehmet İkbal Özmen, Hasan Burak Yel, Fatma Nur Dumlupınar Keşir, Mutlu Polatcan and Emre...

Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories

Arshag DanageozianHearne Institute for Theoretical Physics, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana 70803, USAFind this paper interesting or want...

Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning | Amazon Web Services

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires...

Simplify data prep for generative AI with Amazon SageMaker Data Wrangler | Amazon Web Services

Generative artificial intelligence (generative AI) models have demonstrated impressive capabilities in generating high-quality text, images, and other content. However, these models require massive amounts...

Use foundation models to improve model accuracy with Amazon SageMaker | Amazon Web Services

Photo by Scott Webb on Unsplash Determining the value of housing is a classic example of using machine learning (ML). A...

Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning | Amazon Web Services

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up...

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD | Amazon Web Services

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is...

How To Use Artificial Intelligence To Make More Profitable Trades?

<!-- -->AI has rapidly transformed the financial industry, and trading is no exception. AI-powered trading platforms can help traders make...

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...

Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler | Amazon Web Services

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many...

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