Generative Data Intelligence

Tag: Data Preprocessing

Technology Innovation Institute trains the state-of-the-art Falcon LLM 40B foundation model on Amazon SageMaker | Amazon Web Services

This blog post is co-written with Dr. Ebtesam Almazrouei, Executive Director–Acting Chief AI Researcher of the AI-Cross Center Unit and Project Lead for LLM...

Implement a multi-object tracking solution on a custom dataset with Amazon SageMaker | Amazon Web Services

The demand for multi-object tracking (MOT) in video analysis has increased significantly in many industries, such as live sports, manufacturing, and traffic monitoring. For...

Analyze Amazon SageMaker spend and determine cost optimization opportunities based on usage, Part 3: Processing and Data Wrangler jobs | Amazon Web Services

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. Since its introduction, we’ve helped hundreds of customers...

Analyze Amazon SageMaker spend and determine cost optimization opportunities based on usage, Part 1 | Amazon Web Services

Cost optimization is one of the pillars of the AWS Well-Architected Framework, and it’s a continual process of refinement and improvement over the span...

Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator | Amazon Web Services

This is a guest blog post co-written with Vik Pant and Kyle Bassett from PwC. With organizations increasingly investing in machine learning (ML), ML...

How Vericast optimized feature engineering using Amazon SageMaker Processing

This post is co-written by Jyoti Sharma and Sharmo Sarkar from Vericast. For any machine learning (ML) problem, the data scientist begins by working...

Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

One of the most popular models available today is XGBoost. With the ability to solve various problems such as classification and regression, XGBoost has...

Implementing Other SVM Flavors with Python’s Scikit-Learn

IntroductionThis guide is the third and final part of three guides about Support Vector Machines (SVMs). In this guide, we will keep working with...

How RallyPoint and AWS are personalizing job recommendations to help military veterans and service providers transition back into civilian life using Amazon Personalize

This post was co-written with Dave Gowel, CEO of RallyPoint. In his own words, “RallyPoint is an online social and professional network for veterans,...

Authoring custom transformations in Amazon SageMaker Data Wrangler using NLTK and SciPy

“Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and...

Implementing SVM and Kernel SVM with Python’s Scikit-Learn

IntroductionThis guide is the first part of three guides about Support Vector Machines (SVMs). In this series, we will work on a forged bank...

How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

This is a guest blog post co-written with Hussain Jagirdar from Games24x7. Games24x7 is one of India’s most valuable multi-game platforms and entertains over...

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