AWS Marketplace

Category: Amazon SageMaker

Using PredictHQ data from AWS Data Exchange for demand forecasting

Food supply chain optimization using PredictHQ intelligent event data from AWS Data Exchange for demand forecasting

In this post, we will show how it may be possible to avoid wastage and better forecast food needs using PredictHQ’s dataset with  Amazon Forecast and other machine learning services.

Masking Patient Data with DataMasque's template for Amazon HealthLake

Masking Patient Data with DataMasque’s template for Amazon HealthLake

In this post, Brian, Snehanshu, and I’ll show you how to mask healthcare data for regulatory compliance using Amazon HealthLake and DataMasque.

Track machine learning experiments using InfinStor MLflow with Amazon SageMaker Studio

Track machine learning experiments using InfinStor MLflow with Amazon SageMaker Studio

In this post, I show how to use InfinStor MLflow with Amazon SageMaker Studio to experiment, collaborate, train, and run inferences using this ML platform. With this solution, you do not need to write special code for experiment tracking or model management. You can also share experiments and models with authorized colleagues. SageMaker Studio provides the Notebook and remote IPython kernel portion of the solution, and InfinStor MLflow provides the experiment tracking and model management.

How to discover and use Open Data on AWS Data Exchange

How to discover and use Open Data on AWS Data Exchange

In this blog post, Jeff, Mike, and I will show you how to discover and use no-cost open data datasets on AWS Data Exchange. We will also show you how to enrich the open data with a paid dataset and how to import these datasets into Amazon SageMaker and do an analysis against them.

right sizing sagemaker endpoints

Rightsizing Amazon SageMaker endpoints

As AWS consultants, Victor and I often get asked about recommendations on the right instance configuration to use for real-time inference. Finding the correct instance size to host your trained machine learning (ML) models might be a challenging task. However, choosing the right instance and auto scaling configuration can help reduce model serving costs without […]

decade of innovating with AWS Marketplace

A decade of innovating with AWS Marketplace

Ten years ago today, we launched AWS Marketplace to give builders a simple ecommerce experience to find, buy, and deploy software that runs on AWS. With just a few clicks, builders could find machine images pre-built with multiple operating systems, web servers, network firewalls, databases, content management systems, and more. They could then buy those […]

Using Shutterstock's image datasets to train your computer vision models

Using Shutterstock’s image datasets to train your computer vision models

Image classification and object detection technology allows you to build scalable artificial intelligence models for business cases like visual search, product recommendations, autonomous vehicle object recognition, content moderation, and more. Today, services like Amazon Rekognition offer APIs to perform image analysis and object recognition. However, if your use case requires a more custom image classification […]

Implicit BPR improving recommendations

Improving personalized ranking in recommender systems with Implicit BPR and 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 models java microservices

Integrating machine learning models into your Java-based microservices

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 […]

monitoring data in third party models amazon sagemakermodel monitor

Monitoring data quality in third-party models with Amazon SageMaker Model Monitor

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 […]