AWS Machine Learning Blog

Category: Artificial Intelligence

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

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 […]

Introducing Fortuna: A library for uncertainty quantification

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 […]

Best practices for Amazon SageMaker Training Managed Warm Pools

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, […]

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

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, […]

Augment fraud transactions using synthetic data in Amazon SageMaker

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 […]

LightOn Lyra-fr model is now available on Amazon SageMaker

We are thrilled to announce the availability of the LightOn Lyra-fr foundation model for customers using Amazon SageMaker. LightOn is a leader in building foundation models specializing in European languages. Lyra-fr is a state-of-the-art French language model that can be used to build conversational AI, copywriting tools, text classifiers, semantic search, and more. You can […]

Automatically identify languages in multi-lingual audio using Amazon Transcribe

If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages. Participants may be speaking entirely different languages or may switch between languages. Consider a customer service call to report a problem in an area with a substantial multi-lingual population. Although the conversation could begin […]

Translate multiple source language documents to multiple target languages using Amazon Translate

Enterprises need to translate business-critical content such as marketing materials, instruction manuals, and product catalogs across multiple languages to communicate with a global audience of customers, partners, and stakeholders. Identifying the source language in each document before calling a translate job creates complexities and adds another step to your workflow. For example, an international product […]

Introducing Amazon SageMaker Data Wrangler’s new embedded visualizations

Manually inspecting data quality and cleaning data is a painful and time-consuming process that can take a huge chunk of a data scientist’s time on a project. According to a 2020 survey of data scientists conducted by Anaconda, data scientists spend approximately 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), […]

Start your successful journey with time series forecasting with Amazon Forecast

Organizations of all sizes are striving to grow their business, improve efficiency, and serve their customers better than ever before. Even though the future is uncertain, a data-driven, science-based approach can help anticipate what lies ahead to successfully navigate through a sea of choices. Every industry uses time series forecasting to address a variety of […]