May 2026

Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative AI development that help data scientists and ML engineers accelerate their AI initiatives from experimentation to production. In this post, we’ll explore what’s […]

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Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative AI development that help data scientists and ML engineers accelerate their AI initiatives from experimentation to production. In this post, we’ll explore what’s […]

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Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarcity of transition state configurations in candidate pools. Active learning (AL) can mitigate these costs, but its effectiveness hinges on the acquisition rule. We investigate whether the latent space of a pretrained MLIP already contains the information necessary for effective acquisition, eliminating the need for auxiliary uncertainty heads, Bayesian training and fine-tuning, or committee ensembles. We […]

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Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal information. We introduce PHALAR, a contrastive framework achieving a relative accuracy increase of up to $approx 70%$ over the state-of-the-art while requiring $<50%$ of the parameters and a 7$times$ training speedup. By utilizing a Learned Spectral Pooling layer and a complex-valued head, PHALAR enforces pitch-equivariant and phase-equivariant biases. PHALAR establishes new retrieval state-of-the-art across […]

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Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most of these models use independent one-dimensional (1D) convolutional layers along the spatial and temporal dimensions, which are concatenated without a non-linear activation layer between. In this paper, we investigate an alternative encoding that operates a bi-dimensional (2D) spatiotemporal convolution. While 2D convolutions are numerically identical to two concatenated 1D convolutions along the two dimensions, the impact […]

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Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forgetting prior knowledge. To address this, models are typically fine-tuned using both new task data and a memory buffer of past tasks. However, CLIP’s contrastive loss suffers when the memory buffer is small, leading to performance degradation on previous tasks. We propose a memory-efficient, distributionally robust method that dynamically reweights losses per class during training. Our approach, tested on class […]

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After watching this video course, you’ll be able to use Codex CLI to add features to a Python project directly from your terminal. Codex CLI is an AI-powered coding assistant that runs inside your terminal. It understands your project structure, reads your files, and proposes multi-file changes using natural language instructions. Instead of copying code from a browser or relying on an IDE plugin, you’ll use Codex CLI to implement a real feature in a multi-file Python project […]

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When Amazon places a purchase order with a vendor, a deceptively simple question arises: how many units should go to which fulfillment center, and when? Amazon optimizes this decision based on its demand forecasts, inventory positions, and transportation costs. The vendor, meanwhile, has its own production schedules, warehouse locations, and shipping economics. Each side optimizes independently, and the result is often a plan that is suboptimal for both, resulting in higher costs for all. This is a classical […]

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