Tag: Amazon Sagemaker

A recommender system is an automated software mechanism that uses algorithms and data to personalize product discovery for a particular user. Its essential task is to help users discover the most relevant items within an often-unmanageable set of choices. These days, recommender systems are employed in diverse domains to promote products on e-commerce sites, such […]

Machine learning (ML) enables you to deliver more value to your customers by using your data to automate decisions and transform your business. Pre-trained ML models can speed outcomes for real-time object and person detection, optical character recognition, and other use cases. By performing inferences on an ML model in the application’s workflow, you can […]

Building, training, and deploying machine learning models from scratch can be a time-consuming and costly endeavor for some customers. Moreover, once deployed to production, machine learning models need to be continuously monitored for deviations in model and data quality. To help you expedite model deployment and implement a model monitoring solution, you can integrate pre-trained […]

Pre-trained machine learning (ML) models available in AWS Marketplace take care of the heavy lifting, helping you deliver Artificial Intelligence (AI)- and ML-powered features faster and at a lower cost. However, just like all ML models, sometimes ML model predictions are just not confident enough. You want a pair of human eyes to confirm the […]

This is the second article of a two-part series. Part 1 covered data preparation for machine learning (ML) by using Trifacta. Part 2 covers training the model using Amazon SageMaker Autopilot and operationalizing the workflow. Background ML provides value to business by offering accurate insights to guide business decisions. Gathering insights from ML should be […]

Recently, AWS announced the release of TorchServe, a PyTorch open-source project in collaboration with Facebook. PyTorch is an open-source machine learning framework created by Facebook, which is popular among ML researchers and data scientists. Despite its ease of use and “Pythonic” interface, deploying and managing models in production is still difficult as it requires data scientists to […]

By guest authors Anton Chudaev, DevOps Competence Manager at VITech, Yevheniia Minaieva, Product marketing lead at VITech Lab, and Dmitry Spodarets, Head of VITech Lab When managing Personal Protective Equipment (PPЕ) compliance, temporary absence of safety engineers and human fatigue can lead to unnoticed safety rule breaches. VITech Lab offers a solution to help healthcare […]

For many of my customers, use of machine learning and advanced analytics is a competitive differentiator. To do machine learning (ML) and analytics at scale, you need access to high-quality data sets. While some data is available in house, data scientists often need high-quality, third-party data in addition to the data they have available for […]

By guest author Nick Payton, Head of Marketing and Partnerships, SigOpt The quality of predictions from machine learning (ML) models depends on a few factors. These factors include a high volume of well-prepared data, a robust feature set with the appropriate architecture, and the configuration of hyperparameters. Hyperparameters are numeric values with high and low […]

Organizations of all sizes are realizing that Machine Learning is more than a nice-to-have capability. It’s becoming a necessary differentiator that has the potential to impact almost every aspect of the business. From back-office optimizations to business forecasting and risk reduction, ML is critical for companies looking to innovate and remain relevant. One way for […]