AWS Marketplace

Category: Amazon SageMaker

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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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Experimenting with GPT-2 XL machine learning model package on Amazon SageMaker

New deep-learning model architectures push what’s possible in the field of natural language processing (NLP). NLP is the study of methods of processing and analysis of human language data. In machine learning (ML), transfer learning takes model parameters learned on one task and uses them as a basis for another task with some additional fine-tuning. […]

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Deploying AWS Marketplace ML models

Using Amazon Augmented AI with AWS Marketplace machine learning models

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 […]

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trifacta sagemaker machine learning pt 2

Simplifying machine learning operations with Trifacta and Amazon SageMaker (Part 2)

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 […]

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AWS Marketplace reInvent 2020

AWS Marketplace at re:Invent, week of December 14

Here are AWS Marketplace sessions you can register for and view at re:Invent this week! Each session is held three times, in Pacific Standard Time (PST), Singapore Standard Time (SST), and London Greenwich Mean Time (GMT) time zones. Be sure to check the linked catalog listing, as dates and times may change. MKT208: Use data […]

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