NEWS IN BRIEF: AI/ML FRESH UPDATES

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Amazon Bedrock Flows: Long-running Execution Now Possible!

Amazon Bedrock Flows introduces long-running execution flows, extending workflow time from 5 minutes to 24 hours. This feature allows for processing large datasets, orchestrating multi-step AI workflows, and providing observability for complex generative AI applications.

Mastering AI Optimization with SageMaker

This post delves into LLM development on Amazon SageMaker AI, discussing core lifecycle stages, fine-tuning methodologies like LoRA and QLoRA, and alignment techniques such as RLHF and DPO. It emphasizes knowledge distillation, mixed precision training, and gradient accumulation to optimize memory usage and batch processing for large AI models.

Federated Flower: Revolutionizing Fraud Detection on Amazon SageMaker

Financial institutions face challenges in fraud detection, but with federated learning on Amazon SageMaker AI, they can jointly train models without sharing raw data, boosting accuracy while maintaining compliance. The Flower framework stands out for its ability to integrate with various tools, improving fraud detection accuracy and adhering to industry regulations.

AI Unlocks Hidden Cell Subtypes for Precision Medicine

New AI tool CellLENS combines RNA, protein, and spatial data to group cancer cells based on biology, aiding targeted therapy development. Collaboration between MIT, Harvard, Yale, Stanford, and UPenn leads to breakthrough in understanding immune cell behavior in cancer.

AI-Generated Child Abuse Videos on the Rise

Internet Watch Foundation verified 1,286 AI-made videos in first half of year, mostly in worst category of abuse. AI videos of child sexual abuse have surged, becoming indistinguishable from real imagery, according to the foundation.

Boost Your AI Workflows with SageMaker Studio in Visual Studio Code

AI developers and ML engineers can now utilize Amazon SageMaker Studio in their local VS Code setup, preserving productivity tools and accessing compute resources. The integration simplifies operations, ensures enterprise-grade security, and bridges the gap between local development preferences and cloud-based ML resources.