Category: Amazon SageMaker

In this tutorial, we will walk through the entire machine learning (ML) lifecycle and show you how to architect and build an ML use case end to end using Amazon SageMaker. Amazon SageMaker provides a rich set of capabilities that enable data scientists, machine learning engineers, and developers to prepare, build, train, and deploy ML […]

This post is co-authored by Jiahang Zhong, Head of Data Science at Zopa.  Zopa is a UK-based digital bank and peer to peer (P2P) lender. In 2005, Zopa launched the first ever P2P lending company to give people access to simpler, better-value loans and investments. In 2020, Zopa received a full bank license to offer […]

Time series forecasting is an approach to predict future data values by analyzing the patterns and trends in past observations over time. Organizations across industries require time series forecasting for a variety of use cases, including seasonal sales prediction, demand forecasting, stock price forecasting, weather forecasting, financial planning, and inventory planning. Various cutting edge algorithms […]

Given the rising demand for fast and effective COVID-19 detection, customers are exploring the usage of respiratory sound data, like coughing, breathing, and counting, to automatically diagnose COVID-19 based on machine learning (ML) models. University of Cambridge researchers built a COVID-19 sound application and demonstrated that a simple binary ML classifier can classify healthy and […]

As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to […]

In this post, we discuss solving numerical optimization problems using the very flexible Amazon SageMaker Processing API. Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. This pattern is relevant to solving business-critical problems such as scheduling, routing, allocation, shape optimization, trajectory optimization, […]

As businesses and IT leaders look to accelerate the adoption of machine learning (ML) and artificial intelligence (AI), there is a growing need to understand how to build secure and compliant ML environments that meet enterprise requirements. One major challenge you may face is integrating ML workflows into existing IT and business work streams. A […]

The ability to rapidly iterate and train machine learning (ML) models is key to deriving business value from ML workloads. Because ML models often have many tunable parameters (known as hyperparameters) that can influence the model’s ability to effectively learn, data scientists often use a technique known as hyperparameter optimization (HPO) to achieve the best-performing […]

With the rapid growth of object detection techniques, several frameworks with packaged pre-trained models have been developed to provide users easy access to transfer learning. For example, GluonCV, Detectron2, and the TensorFlow Object Detection API are three popular computer vision frameworks with pre-trained models. In this post, we use Amazon SageMaker to build, train, and […]

Machine learning (ML)-based solutions are capable of solving complex problems, from voice recognition to finding and identifying faces in video clips or photographs. Usually, these solutions use large amounts of training data, which results in a model that processes input data and produces numeric output that can be interpreted as a word, face, or classification […]