AWS Machine Learning Blog

Category: Artificial Intelligence

Forecasting AWS spend using the AWS Cost and Usage Reports, AWS Glue DataBrew, and Amazon Forecast

AWS Cost Explorer enables you to view and analyze your AWS Cost and Usage Reports (AWS CUR). You can also predict your overall cost associated with AWS services in the future by creating a forecast of AWS Cost Explorer, but you can’t view historical data beyond 12 months. Moreover, running custom machine learning (ML) models […]

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Managing your machine learning lifecycle with MLflow and Amazon SageMaker

With the rapid adoption of machine learning (ML) and MLOps, enterprises want to increase the velocity of ML projects from experimentation to production. During the initial phase of an ML project, data scientists collaborate and share experiment results in order to find a solution to a business need. During the operational phase, you also need […]

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The following diagram illustrates some of the services that can be integrated with SageMaker Feature Store.

Understanding the key capabilities of Amazon SageMaker Feature Store

One of the challenging parts of machine learning (ML) is feature engineering, the process of transforming data to create features for ML. Features are processed data signals used for training ML models and for deployed models to make accurate predictions. Data scientists and ML engineers can spend up to 60-70% of their time on feature […]

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Saving time with personalized videos using AWS machine learning

CLIPr aspires to help save 1 billion hours of people’s time. We organize video into a first-class, searchable data source that unlocks the content most relevant to your interests using AWS machine learning (ML) services. CLIPr simplifies the extraction of information in videos, saving you hours by eliminating the need to skim through them manually […]

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The first screenshot shows the profile while running with DP.

Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA

This is a guest post from deepset (creators of the open source frameworks FARM and Haystack), and was contributed to by authors from NVIDIA and AWS.  At deepset, we’re building the next-level search engine for business documents. Our core product, Haystack, is an open-source framework that enables developers to utilize the latest NLP models for […]

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How to deliver natural conversational experiences using Amazon Lex Streaming APIs

Natural conversations often include pauses and interruptions. During customer service calls, a caller may ask to pause the conversation or hold the line while they look up the necessary information before continuing to answer a question. For example, callers often need time to retrieve credit card details when making bill payments. Interruptions are also common. […]

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Model serving in Java with AWS Elastic Beanstalk made easy with Deep Java Library

Deploying your machine learning (ML) models to run on a REST endpoint has never been easier. Using AWS Elastic Beanstalk and Amazon Elastic Compute Cloud (Amazon EC2) to host your endpoint and Deep Java Library (DJL) to load your deep learning models for inference makes the model deployment process extremely easy to set up. Setting […]

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We can improve the accuracy by retraining the model with more video files.

Building your own brand detection and visibility using Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels – Part 1: End-to-end solution

According to Gartner, 58% of marketing leaders believe brand is a critical driver of buyer behavior for prospects, and 65% believe it’s a critical driver of buyer behavior for existing customers. Companies spend huge amounts of money on advertisement to raise brand visibility and awareness. In fact, as per Gartner, CMO spends over 21% of […]

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Model serving made easier with Deep Java Library and AWS Lambda

Developing and deploying a deep learning model involves many steps: gathering and cleansing data, designing the model, fine-tuning model parameters, evaluating the results, and going through it again until a desirable result is achieved. Then comes the final step: deploying the model. AWS Lambda is one of the most cost effective service that lets you run code without […]

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The following diagram illustrates the main steps you need to complete in order to create and publish your custom SageMaker project template.

Multi-account model deployment with Amazon SageMaker Pipelines

Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps). Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account […]

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