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Distributed data preprocessing with GKE and Ray: Scaling for the enterprise

The exponential growth of machine learning models brings with it ever-increasing datasets. This data deluge creates a significant bottleneck in the Machine Learning Operations (MLOps) lifecycle, as traditional data preprocessing methods struggle to scale. The preprocessing phase, which is critical for transforming raw data into a format suitable for model training, can become a major …

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Align and monitor your Amazon Bedrock powered insurance assistance chatbot to responsible AI principles with AWS Audit Manager

Generative AI applications are gaining widespread adoption across various industries, including regulated industries such as financial services and healthcare. As these advanced systems accelerate in playing a critical role in decision-making processes and customer interactions, customers should work towards ensuring the reliability, fairness, and compliance of generative AI applications with industry regulations. To address this …

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Supervised Fine Tuning for Gemini: A best practices guide

Foundation models such as Gemini have revolutionized how we work, but sometimes they need guidance to excel at specific business tasks. Perhaps their answers are too long, or their summaries miss the mark. That’s where supervised fine-tuning (SFT) comes in. When done right, it unlocks incredible precision to tailor Gemini for specialized tasks, domains, and …

3D Shape Tokenization

We introduce Shape Tokens, a 3D representation that is continuous, compact, and easy to integrate into machine learning models. Shape Tokens serve as conditioning vectors, representing shape information within a 3D flow-matching model. This flow-matching model is trained to approximate probability density functions corresponding to delta functions concentrated on the surfaces of 3D shapes. By …

Title Launch Observability at Netflix Scale

Part 2: Navigating Ambiguity By: Varun Khaitan With special thanks to my stunning colleagues: Mallika Rao, Esmir Mesic, Hugo Marques Building on the foundation laid in Part 1, where we explored the “what” behind the challenges of title launch observability at Netflix, this post shifts focus to the “how.” How do we ensure every title launches seamlessly …

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Efficiently build and tune custom log anomaly detection models with Amazon SageMaker

In this post, we walk you through the process to build an automated mechanism using Amazon SageMaker to process your log data, run training iterations over it to obtain the best-performing anomaly detection model, and register it with the Amazon SageMaker Model Registry for your customers to use it. Log-based anomaly detection involves identifying anomalous …

The PyTorch developer’s guide to JAX fundamentals

Like many PyTorch users, you may have heard great things about JAX — its high performance, the elegance of its functional programming approach, and its powerful, built-in support for parallel computation. However, you may have also struggled to find what you need to get started: a straightforward, easy-to-follow tutorial to help you understand the basics …

Part 2: A Survey of Analytics Engineering Work at Netflix

This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Need to catch up? Check out Part 1. In this article, we highlight a few exciting analytic business applications, and in our final article we’ll go …

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Telegram Chatbots: Are They a Good Fit for Your Business?

Telegram chatbots are rapidly gaining traction, with over 1.5 million bots already created. As one of the fastest-growing messaging platforms, Telegram boasts a user base exceeding 550 million globally, offering businesses an unparalleled opportunity to engage with their audience effectively. In an era where customers prefer direct communication, research from Social Media Today reveals that …