AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

Medical imaging AI researchers and developers need a scalable, enterprise framework to build, deploy, and integrate their AI applications. AWS and NVIDIA have come together to make this vision a reality. AWS HealthImaging (AHI) is a HIPAA-eligible, highly scalable, performant, and cost-effective medical imagery store.

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AWS Machine Learning Blog

MAY 23, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are rapidly adopting and using ML technologies to transform their businesses.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Instance types For our evaluation, we considered two comparable AWS CPU instances: a c6i.8xlarge 8xlarge powered by AWS Graviton3.

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AWS Machine Learning Blog

JANUARY 17, 2024

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

In either case, as knowledge management becomes more complex, generative AI presents a game-changing opportunity for enterprises to connect people to the information they need to perform and innovate. To help tackle this challenge, Accenture collaborated with AWS to build an innovative generative AI solution called Knowledge Assist.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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AWS Machine Learning Blog

DECEMBER 14, 2023

This is a customer post jointly authored by ICL and AWS employees. To overcome this business challenge, ICL decided to develop in-house capabilities to use machine learning (ML) for computer vision (CV) to automatically monitor their mining machines.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It features an ML package with machine learning-specific APIs that enable the easy creation of ML models, training, and deployment.

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AWS Machine Learning Blog

APRIL 18, 2024

This post is co-authored by Jackie Rocca, VP of Product, AI at Slack Slack is where work happens. It’s the AI-powered platform for work that connects people, conversations, apps, and systems together in one place. None of your data is used to train the underlying models.

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AWS Machine Learning Blog

MAY 25, 2023

This post was co-authored by Brian Curry (Founder and Head of Products at OCX Cognition) and Sandhya MN (Data Science Lead at InfoGain) OCX Cognition is a San Francisco Bay Area-based startup, offering a commercial B2B software as a service (SaaS) product called Spectrum AI.

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IBM Data Science in Practice

MARCH 8, 2023

You can then export the model and deploy it on Amazon Sagemaker on Amazon Web Server (AWS). This article shows how you can use a no-code or all-code approach for training a machine learning model in Watson Studio that you can then deploy on AWS. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

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AWS Machine Learning Blog

MAY 4, 2023

Generative AI is gaining a lot of public attention at present, with talk around products such as GPT4, ChatGPT, DALL-E2, Bard, and many other AI technologies. Many customers have been asking for more information on AWS’s generative AI solutions. and other generative AI models. and other generative AI models.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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AWS Machine Learning Blog

MARCH 3, 2023

Machine learning (ML) can help companies make better business decisions through advanced analytics. Companies across industries apply ML to use cases such as predicting customer churn, demand forecasting, credit scoring, predicting late shipments, and improving manufacturing quality.

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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

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. We also deep dive into the most common architectures and AWS resources to facilitate these integrations.

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Towards AI

NOVEMBER 5, 2023

Last Updated on November 5, 2023 by Editorial Team Author(s): Euclidean AI Originally published on Towards AI. Source: [link] This article describes a solution for a generative AI resume screener that got us 3rd place at DataRobot & AWS Hackathon 2023. AWS Bedrock provides a Python SDK named Boto3.

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Flipboard

NOVEMBER 22, 2023

The Amazon Web Services (AWS) Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS. The full list of publicly available datasets are on the Registry of Open Data on AWS and also discoverable on the AWS Data Exchange. This quarter, AWS released 34 new or updated datasets.

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AWS Machine Learning Blog

JANUARY 13, 2023

In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. We have developed an FL framework on AWS that enables analyzing distributed and sensitive health data in a privacy-preserving manner. In this post, we showed how you can deploy the open-source FedML framework on AWS.

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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

AUGUST 30, 2023

To solve this problem, we propose the use of generative AI, a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services.

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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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IBM Journey to AI blog

JULY 19, 2023

The exponential leap in generative AI is already transforming many industries: optimizing workflows , helping human teams focus on value added tasks and accelerating time to market. Life sciences industry is beginning to take notice and aims to leapfrog the technological advances.

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AWS Machine Learning Blog

SEPTEMBER 26, 2023

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This also helps keep the pipeline well structured.

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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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Towards AI

APRIL 4, 2023

Last Updated on April 4, 2023 by Editorial Team Introducing a Python SDK that allows enterprises to effortlessly optimize their ML models for edge devices. Edge Impulse is known for its innovative tools that have greatly lowered the barrier to building edge AI solutions for digital health and industrial productivity.

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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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AWS Machine Learning Blog

FEBRUARY 19, 2024

Model server overview A model server is a software component that provides a runtime environment for deploying and serving machine learning (ML) models. The primary purpose of a model server is to allow effortless integration and efficient deployment of ML models into production systems. For MMEs, each model.py The full model.py

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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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Mlearning.ai

APRIL 28, 2023

But there are some strategies that artificial intelligence(AI) developers can implement to optimize and decrease execution time for Python machine learning (ML) models, for instance: Using binary formats for saving models Saving machine learning models in binary formats like .pkl, pb can decrease execution time for Python.

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AWS Machine Learning Blog

MAY 22, 2024

Using machine learning (ML) and natural language processing (NLP) to automate product description generation has the potential to save manual effort and transform the way ecommerce platforms operate. With the advancement of Generative AI , we can use vision-language models (VLMs) to predict product attributes directly from images.

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Towards AI

JUNE 24, 2023

Last Updated on June 28, 2023 by Editorial Team Author(s): Anirudh Mehta Originally published on Towards AI. The ’31 Questions that Shape Fortune 500 ML Strategy’ highlighted key questions to assess the maturity of an ML system. A robust ML platform offers managed solutions to easily address these aspects.

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present 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. In the following sections, we discuss the stages of the process in detail.

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AWS Machine Learning Blog

MAY 1, 2024

In this post, we show how Amazon Bedrock and Amazon SageMaker Canvas , a no-code AI suite, allow business users without deep technical expertise to fine-tune and deploy LLMs. For this example, we enter the question, “Who developed the lie-detecting algorithm Fraudoscope?” Amazon Titan responded incorrectly and verbosely.

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AWS Machine Learning Blog

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s.

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