AWS Machine Learning Blog

MAY 10, 2023

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. New generations of CPUs offer a significant performance improvement in ML inference due to specialized built-in instructions. 4xlarge instances. 4xlarge instances.

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AWS Machine Learning Blog

FEBRUARY 21, 2023

We’re thrilled to announce an expanded collaboration between AWS and Hugging Face to accelerate the training, fine-tuning, and deployment of large language and vision models used to create generative AI applications. AWS has a deep history of innovation in generative AI. Or they can self-manage on Amazon EC2.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions to make healthcare more accessible, affordable, and efficient by accelerating cloud connectivity of enterprise imaging. AHI provides API access to ImageSet metadata and ImageFrames.

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Towards AI

NOVEMBER 5, 2023

Source: [link] This article describes a solution for a generative AI resume screener that got us 3rd place at DataRobot & AWS Hackathon 2023. You can also set the environment variables on the notebook instance for things like AWS access key etc. Source: author’s screenshot on AWS We used Anthropic Claude 2 in our solution.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud.

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AWS Machine Learning Blog

MAY 3, 2023

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 DLCs are available on Amazon Elastic Container Registry (Amazon ECR) for AWS Graviton or x86. is up to 3.5

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AWS Machine Learning Blog

NOVEMBER 21, 2023

Amazon SageMaker provides an end-to-end set of services that allow Amazon Music to build, train, and deploy on the AWS Cloud with minimal effort. By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure.

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AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Store your Snowflake account credentials in AWS Secrets Manager.

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Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. If not, refer to Using the SageMaker Python SDK before continuing.

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AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. To use accelerators, you need a software layer to support them.

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AWS Machine Learning Blog

JULY 24, 2023

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. In this post we walk you through the process of deploying FastAPI model servers on AWS Inferentia devices (found on Amazon EC2 Inf1 and Amazon EC Inf2 instances).

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Smart Data Collective

FEBRUARY 20, 2022

AWS (Amazon Web Services), the comprehensive and evolving cloud computing platform provided by Amazon, is comprised of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS). With its wide array of tools and convenience, AWS has already become a popular choice for many SaaS companies.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

Volume of corner cases – ML models need to handle a wide range of corner cases. SageMaker is a fully managed machine learning (ML) service. Data parallelism supports popular deep learning frameworks PyTorch, PyTorch Lightening, TensorFlow, and Hugging Face Transformers.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football.

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AWS Machine Learning Blog

FEBRUARY 7, 2024

In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Set up permissions that allows your AWS account to access Amazon Fraud Detector.

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Mlearning.ai

JANUARY 28, 2023

From Sale Marketing Business 7 Powerful Python ML For Data Science And Machine Learning need to be use. This post will outline seven powerful python ml libraries that can help you in data science and different python ml environment. A python ml library is a collection of functions and data that can use to solve problems.

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Mlearning.ai

FEBRUARY 2, 2023

How I cleared AWS Machine Learning Specialty with three weeks of preparation (I will burst some myths of the online exam) How I prepared for the test, my emotional journey during preparation, and my actual exam experience Certified AWS ML Specialty Badge source Introduction:- I recently gave and cleared AWS ML certification on 29th Dec 2022.

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AWS Machine Learning Blog

MAY 2, 2024

Amazon Personalize makes it straightforward to personalize your website, app, emails, and more, using the same machine learning (ML) technology used by Amazon, without requiring ML expertise. For Recipe , choose the new aws-user-personalization-v2 recipe. A solution version refers to a trained ML model.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

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AWS Machine Learning Blog

APRIL 5, 2024

It’s based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily. It requires no machine learning (ML) expertise to use and we’re continually adding new computer vision features to the service. session.Session().region_name

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AWS Machine Learning Blog

APRIL 18, 2023

To improve this experience for its members, we at RallyPoint wanted to explore how machine learning (ML) could help. The following sections cover the business and technical challenges, the approach taken by the AWS and RallyPoint teams, and the performance of implemented solution that leverages Amazon Personalize.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. The endpoint uniformly distributes incoming requests to ML instances using a round-robin algorithm.

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AWS Machine Learning Blog

MARCH 20, 2023

Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. with PyTorch (v1.11) being the ML framework used with Intel® Extension for PyTorch.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Yes, the AWS re:Invent season is upon us and as always, the place to be is Las Vegas! Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. This year, learn about LLMOps, not just MLOps! are the sessions dedicated to AWS DeepRacer !

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AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. Their objective was to fine-tune an existing computer vision machine learning (ML) model for SKU detection. Log model training metrics.

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AWS Machine Learning Blog

JUNE 8, 2023

Data scientists need a consistent and reproducible environment for machine learning (ML) and data science workloads that enables managing dependencies and is secure. AWS Deep Learning Containers already provides pre-built Docker images for training and serving models in common frameworks such as TensorFlow, PyTorch, and MXNet.

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AWS Machine Learning Blog

DECEMBER 12, 2023

The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can use this technology to create new products and services on AWS, such as enterprise chatbots, which are more conversational. Optionally, deploy the application using AWS Amplify.

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AWS Machine Learning Blog

AUGUST 3, 2023

Computer vision (CV) is one of the most common applications of machine learning (ML) and deep learning. We demonstrate how you can combine well-known ML solutions with postprocessing to address this problem on the AWS Cloud. We use deep learning models to solve this problem.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

In this post, we discuss deploying scalable machine learning (ML) models for diarizing media content using Amazon SageMaker , with a focus on the WhisperX model. This S3 bucket was configured to emit an event when new files are detected within it, triggering an AWS Lambda function. __dict__[WAV2VEC2_MODEL].get_model(dl_kwargs={"model_dir":

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AWS Machine Learning Blog

DECEMBER 5, 2023

Walkthrough AWS-optimized AllGather AWS-optimized AllGather uses the following techniques to achieve better performance on AWS infrastructure compared to NCCL: We move data between instances via Elastic Fabric Adapter (EFA) network with an all-to-all communication pattern.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach to develop a new AI/ML enabled solution for their customers to tackle the “blank canvas” problem. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale.

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AWS Machine Learning Blog

MARCH 23, 2023

This is joint post co-written by Leidos and AWS. Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where you build cloud-enabled, encrypted pipelines. In the following sections, we walk through the code to build this pipeline.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. You can also use the StartIngestionJob API to trigger the sync via the AWS SDK.

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Data Science 101

NOVEMBER 29, 2019

Amazon Athena and Aurora add support for ML in SQL Queries You can now invoke Machine Learning models right from your SQL Queries. Use Amazon Sagemaker to add ML predictions in Amazon QuickSight Amazon QuickSight, the AWS BI tool, now has the capability to call Machine Learning models.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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