How X (Twitter) Designed Its Home Timeline API: Lessons to Learn | by Oleksii Trekhleb | Dec, 2024
A closer look at X’s API: fetching data, linking entities, and solving under-fetching.When designing a system’s API, software engineers often...
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Accelerate analysis and discovery of cancer biomarkers with Amazon Bedrock Agents
According to the National Cancer Institute, a cancer biomarker is a “biological molecule found in blood, other body fluids, or tissues that...
Credit Card Fraud Detection with Different Sampling Techniques | by Mythili Krishnan | Dec, 2024
Credit card fraud detection is a plague that all financial institutions are at risk with. In general fraud detection is...
How Twitch used agentic workflow with RAG on Amazon Bedrock to supercharge ad sales
Twitch, the world’s leading live-streaming platform, has over 105 million average monthly visitors. As part of Amazon, Twitch advertising is...
Master Machine Learning: 4 Classification Models Made Simple | by Leo Anello 💡 | Dec, 2024
A Beginner’s Guide to Building Models in 15 Practical Steps56 min read·21 hours agoI will now present a template for...
How Clearwater Analytics is revolutionizing investment management with generative AI and Amazon SageMaker JumpStart
This post was written with Darrel Cherry, Dan Siddall, and Rany ElHousieny of Clearwater Analytics. As global trading volumes rise...
How Have Data Science Interviews Changed Over 4 Years? | by Matt Przybyla | Dec, 2024
An aggregated look on the differences between then & now: 2020 vs 2024 — some big frustrations and positive learnings.Yes...
Implementing login node load balancing in SageMaker HyperPod for enhanced multi-user experience
Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models...
Agentic AI: Building Autonomous Systems from Scratch | by Luís Roque | Dec, 2024
A Step-by-Step Guide to Creating Multi-Agent Frameworks in the Age of Generative AI13 min read·18 hours agoThis post was co-authored...
How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and...
Is Complex Writing Nothing But Formulas? | by Vered Zimmerman | Dec, 2024
Text analytics hints at how volumes of writing get createdIn the broadest of strokes, Natural Language Processing transforms language into...
Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 1: ModelTrainer
Amazon SageMaker has redesigned its Python SDK to provide a unified object-oriented interface that makes it straightforward to interact with...
Transformers Key-Value (KV) Caching Explained | by Michał Oleszak | Dec, 2024
LLMOpsSpeed up your LLM inferenceThe transformer architecture is arguably one of the most impactful innovations in modern deep learning. Proposed...
Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 2: ModelBuilder
In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and...
Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models | by Jérôme DIAZ | Dec, 2024
In this article we will explore why 128K tokens (and more) models can’t fully replace using RAG.We’ll start with a...
