AWS Machine Learning Blog

NOVEMBER 13, 2024

The global intelligent document processing (IDP) market size was valued at $1,285 million in 2022 and is projected to reach $7,874 million by 2028 ( source ). To find the URL, open the AWS Management Console for SageMaker and choose Ground Truth and then Labeling workforces in the navigation pane. edit cdk.json Deploy the application.

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AWS Machine Learning Blog

JUNE 23, 2023

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. Additionally, you can use AWS Lambda directly to expose your models and deploy your ML applications using your preferred open-source framework, which can prove to be more flexible and cost-effective.

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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

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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AWS Machine Learning Blog

JANUARY 6, 2023

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. The tools are typically used by data scientists and ML developers from experimentation to production deployment of AI and ML solutions.

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AWS Machine Learning Blog

DECEMBER 4, 2023

In this post, we explain how we built an end-to-end product category prediction pipeline to help commercial teams by using Amazon SageMaker and AWS Batch , reducing model training duration by 90%. An important aspect of our strategy has been the use of SageMaker and AWS Batch to refine pre-trained BERT models for seven different languages.

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AWS Machine Learning Blog

DECEMBER 7, 2023

In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions. AWS Step Functions automatically initiate and monitor these workflows by simplifying error handling.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

In this post, we investigate of potential for the AWS Graviton3 processor to accelerate neural network training for ThirdAI’s unique CPU-based deep learning engine. As shown in our results, we observed a significant training speedup with AWS Graviton3 over the comparable Intel and NVIDIA instances on several representative modeling workloads.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. Multi-modal models that understand diverse data sources can provide more robust forecasts.

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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

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

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Dataconomy

FEBRUARY 6, 2024

With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector. Why does AI/ML deserve to be the future of the modern world? Let’s understand the crucial role of AI/ML in the tech industry.

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AWS Machine Learning Blog

MARCH 14, 2024

Generative AI with AWS The emergence of FMs is creating both opportunities and challenges for organizations looking to use these technologies. Beyond hardware, data cleaning and processing, model architecture design, hyperparameter tuning, and training pipeline development demand specialized machine learning (ML) skills.

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AWS Machine Learning Blog

MAY 8, 2024

In 2022, Dialog Axiata made significant progress in their digital transformation efforts, with AWS playing a key role in this journey. Dialog Axiata runs some of their business-critical telecom workloads on AWS, including Charging Gateway, Payment Gateway, Campaign Management System, SuperApp, and various analytics tasks.

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AWS Machine Learning Blog

APRIL 7, 2025

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

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O'Reilly Media

JANUARY 10, 2023

In reality, 2022 has been a fantastic year for AI. GitHub CoPilot also wasn’t new in 2022, but in the last year we’ve heard of more and more programmers who are using ChatGPT to write production code. NFTs seemed an awful lot like buying a painting and framing the receipt.

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AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. What you get is an ML development environment that is consistent and portable. In this post, we show you how to run your ML training jobs in a container using Amazon ECS to deploy, manage, and scale your ML workload.

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AWS Machine Learning Blog

AUGUST 15, 2024

With over 50 connectors, an intuitive Chat for data prep interface, and petabyte support, SageMaker Canvas provides a scalable, low-code/no-code (LCNC) ML solution for handling real-world, enterprise use cases. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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Smart Data Collective

NOVEMBER 9, 2022

ML opens up new opportunities for computers to solve tasks previously performed by humans and trains the computer system to make accurate predictions when inputting data. Top ML Companies. With ML capabilities, these tools eliminate errors and process data at a rapid pace. Indium Software. Altoros has five global offices.

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AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. MPII’s bid optimization engine solution uses ML models to generate optimal bids for participation in different markets. Data comes from disparate sources in a number of formats.

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AWS Machine Learning Blog

JUNE 26, 2023

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. To address this challenge, AWS introduced Amazon SageMaker Role Manager in December 2022. SageMaker Role Manager also allows for fine-grained customization.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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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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AWS Machine Learning Blog

JANUARY 13, 2023

Since then, TR has achieved many more milestones as its AI products and services are continuously growing in number and variety, supporting legal, tax, accounting, compliance, and news service professionals worldwide, with billions of machine learning (ML) insights generated every year. The challenges. Solution overview.

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AWS Machine Learning Blog

MARCH 11, 2025

OpenAI launched GPT-4o in May 2024, and Amazon introduced Amazon Nova models at AWS re:Invent in December 2024. simple Finance Did meta have any mergers or acquisitions in 2022? The open source version works on a customers AWS account so you can experiment on your AWS account with your proprietary data.

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IBM Journey to AI blog

MAY 26, 2023

The role of AWS and cloud security in life sciences However, with greater power comes great responsibility. According to IBM’s Cost of a data breach 2022 report, 83% of organizations studied have had more than one data breach, and 60% of these breaches caused organizations to pass price increases on to clients.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium —a purpose-built machine learning (ML) accelerator optimized to provide a high-performance, cost-effective, and massively scalable platform for training deep learning models in the cloud. 32xlarge instances.

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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. With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices.

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AWS Machine Learning Blog

JANUARY 21, 2025

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. This visual, point-and-click interface democratizes ML so users can take advantage of the power of AI for various business applications. One of these methods is quantiles.

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AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production.

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Flipboard

MARCH 9, 2023

In this post, we review the technical requirements and application design considerations for fine-tuning and serving hyper-personalized AI models at scale on AWS. For example, NVIDIA Triton Inference Server, a high-performance open-source inference software, was natively integrated into the SageMaker ecosystem in 2022.

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AWS Machine Learning Blog

JANUARY 22, 2025

These services use advanced machine learning (ML) algorithms and computer vision techniques to perform functions like object detection and tracking, activity recognition, and text and audio recognition. An EventBridge rule then triggers the AWS Step Functions workflow to begin processing the video recording into a transcript.

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AWS Machine Learning Blog

MARCH 29, 2023

In this post, we discuss how the IEO developed UNDP’s artificial intelligence and machine learning (ML) platform—named Artificial Intelligence for Development Analytics (AIDA)— in collaboration with AWS, UNDP’s Information and Technology Management Team (UNDP ITM), and the United Nations International Computing Centre (UNICC).

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AWS Machine Learning Blog

JANUARY 26, 2023

Amazon SageMaker is a fully managed machine learning (ML) service providing various tools to build, train, optimize, and deploy ML models. ML insights facilitate decision-making. To assess the risk of credit applications, ML uses various data sources, thereby predicting the risk that a customer will be delinquent.

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AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. It involves training a global machine learning (ML) model from distributed health data held locally at different sites.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

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