Tag: faang

Today, we’re happy to announce updates to our Amazon SageMaker Experiments capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions from any integrated development environment (IDE) using the SageMaker Python SDK or boto3, including local Jupyter Notebooks. Machine learning (ML) is an iterative process. When solving …

Read more “Next generation Amazon SageMaker Experiments – Organize, track, and compare your machine learning trainings at scale”

Proper estimation of predictive uncertainty is fundamental in applications that involve critical decisions. Uncertainty can be used to assess the reliability of model predictions, trigger human intervention, or decide whether a model can be safely deployed in the wild. We introduce Fortuna, an open-source library for uncertainty quantification. Fortuna provides calibration methods, such as conformal …

Read more “Introducing Fortuna: A library for uncertainty quantification”

Amazon SageMaker Training Managed Warm Pools gives you the flexibility to opt in to reuse and hold on to the underlying infrastructure for a user-defined period of time. This is done while also maintaining the benefit of passing the undifferentiated heavy lifting of managing compute instances in to Amazon SageMaker Model Training. In this post, …

Read more “Best practices for Amazon SageMaker Training Managed Warm Pools”

In an increasingly data-centric world, enterprises must focus on gathering both valuable physical information and generating the information that they need but can’t easily capture. Data access, regulation, and compliance are an increasing source of friction for innovation in analytics and artificial intelligence (AI). For highly regulated sectors such as Financial Services, Healthcare, Life Sciences, …

Read more “How to evaluate the quality of the synthetic data – measuring from the perspective of fidelity, utility, and privacy”

Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or …

Read more “Augment fraud transactions using synthetic data in Amazon SageMaker”

(Scroll down for English translation below) De nouvelles infrastructures digitales savamment déployées pourraient-elles rendre les villes plus responsables, durables, efficaces et agréables ? C’est l’idée que poursuivent, depuis près de quinze ans [1], les instigateurs des smart cities ou villes des intelligences. De Marseille à Paris, en passant par Dijon, Lyon, Chartes, Toulouse, Grenoble, Nice, Angers …

Read more “Foundry au service de la décentralisation, différenciation, déconcentration de la donnée”

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use. The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search, and outperforms our previous most capable model, Davinci, at most tasks, while being priced 99.8% lower. Read documentation Embeddings …

Read more “New and Improved Embedding Model”