AWS Machine Learning Blog

NOVEMBER 19, 2024

At the time, I knew little about AI or machine learning (ML). But AWS DeepRacer instantly captured my interest with its promise that even inexperienced developers could get involved in AI and ML. Panic set in as we realized we would be competing on stage in front of thousands of people while knowing little about ML.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. ML SA), Monidipa Chakraborty (Sr. Delete the IAM role you created.

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O'Reilly Media

APRIL 22, 2025

And now for the meta twist: This entire blog post was itself the product of “vibe blogging.” I had enough energy today to outline some rough ideas, then let Claude do the vibe blogging for me, but not enough to fully write, edit, and fret over the wording of a full 2,500-word blog post all by myself.

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AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. In the following example, we show how to fine-tune the latest Meta Llama 3.1

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AWS Machine Learning Blog

DECEMBER 12, 2024

The machine learning (ML) practitioners need to iterate over these settings before finally deploying the endpoint to SageMaker for inference. Over the past 5 years, she has worked with multiple enterprise customers to set up a secure, scalable AI/ML platform built on SageMaker.

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AWS Machine Learning Blog

APRIL 10, 2025

This means users can build resilient clusters for machine learning (ML) workloads and develop or fine-tune state-of-the-art frontier models, as demonstrated by organizations such as Luma Labs and Perplexity AI. Frontier model builders can further enhance model performance using built-in ML tools within SageMaker HyperPod.

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Data Science Dojo

JULY 5, 2024

Modern businesses are embracing machine learning (ML) models to gain a competitive edge. Deploying ML models in their day-to-day processes allows businesses to adopt and integrate AI-powered solutions into their businesses. This reiterates the increasing role of AI in modern businesses and consequently the need for ML models.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Data Science Dojo

FEBRUARY 13, 2025

In this blog, well explore the top AI conferences in the USA for 2025, breaking down what makes each one unique and why they deserve a spot on your calendar. From an enterprise perspective, this conference will help you learn to optimize business processes, integrate AI into your products, or understand how ML is reshaping industries.

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databricks

DECEMBER 12, 2023

Introduction Databricks Lakehouse Monitoring allows you to monitor all your data pipelines – from data to features to ML models – without additional too.

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Flipboard

MAY 15, 2025

Qualtrics harnesses the power of generative AI, cutting-edge machine learning (ML), and the latest in natural language processing (NLP) to provide new purpose-built capabilities that are precision-engineered for experience management (XM). To learn more about how AI is transforming experience management, visit this blog from Qualtrics.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.

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AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. He focuses on architecting and implementing large-scale generative AI and classic ML pipeline solutions.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

With the increasing use of large models, requiring a large number of accelerated compute instances, observability plays a critical role in ML operations, empowering you to improve performance, diagnose and fix failures, and optimize resource utilization. Anjali Thatte is a Product Manager at Datadog.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Challenges in deploying advanced ML models in healthcare Rad AI, being an AI-first company, integrates machine learning (ML) models across various functions—from product development to customer success, from novel research to internal applications. Rad AI’s ML organization tackles this challenge on two fronts.

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Data Science Dojo

SEPTEMBER 23, 2024

In this blog, we will explore the concept of a confusion matrix using a spam email example. Also learn about the Random Forest Algorithm and its uses in ML Scenario: Email Spam Classification Suppose you have built a machine learning model to classify emails as either “Spam” or “Not Spam.”

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AWS Machine Learning Blog

OCTOBER 28, 2024

This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects.

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ML @ CMU

APRIL 9, 2025

In this blog post, we discuss how we designed and deployed Copilot Arena. In contrast, Copilot Arena users are working on a diverse set of realistic tasks, including but not limited to frontend components, backend logic, and ML pipelines. If you think this blog post is useful for your work, please consider citing it.

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databricks

JUNE 4, 2025

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data! REGISTER Ready to get started?

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Towards AI

MAY 1, 2025

In my final year of BTech, with a growing interest in data science and AI/ML, I realized I was unprepared to showcase my knowledge and skills I had built over time. This blog is your step-by-step roadmap to creating a compelling data science portfolio that demonstrates your skillset, highlights your projects, and sets you apart from everyone.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. Both models support a context window of 32,000 tokens, which is roughly 50 pages of text.

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Flipboard

MAY 30, 2025

The quality assurance process includes automated testing methods combining ML-, algorithm-, or LLM-based evaluations. In addition, the process employs traditional ML procedures such as named entity recognition (NER) or estimation of final confidence with regression models. The team extensively used fine-tuned SLMs.

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AWS Machine Learning Blog

DECEMBER 16, 2024

Getting started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. About the authors Marc Karp is an ML Architect with the Amazon SageMaker Service team. He focuses on helping customers design, deploy, and manage ML workloads at scale.

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AWS Machine Learning Blog

APRIL 7, 2025

read()) print(json.dumps(response_body, indent=2)) response = requests.get("[link] blog = response.text chat_with_document(blog, "What is the blog writing about?") For the subsequent request, we can ask a different question: chat_with_document(blog, "what are the use cases?")

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AWS Machine Learning Blog

OCTOBER 15, 2024

We’re excited to announce the release of SageMaker Core , a new Python SDK from Amazon SageMaker designed to offer an object-oriented approach for managing the machine learning (ML) lifecycle. With SageMaker Core, managing ML workloads on SageMaker becomes simpler and more efficient. and above. Any version above 2.231.0

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Flipboard

FEBRUARY 11, 2025

This solution ingests and processes data from hundreds of thousands of support tickets, escalation notices, public AWS documentation, re:Post articles, and AWS blog posts. By using Amazon Q Business, which simplifies the complexity of developing and managing ML infrastructure and models, the team rapidly deployed their chat solution.

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Flipboard

MAY 14, 2025

It combines interactive dashboards, natural language query capabilities, pixel-perfect reporting , machine learning (ML) driven insights, and scalable embedded analytics in a single, unified service. Cross account calls arent supported at the time of writing this blog. The index creation process may take a few minutes to complete.

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Towards AI

JANUARY 6, 2025

Photo by Stefany Andrade on Unsplash Dealing with Box Plots, Violin Plots and Contour Plots reveals a lot about Data before Machine Learning Modeling, Welcome back to the wrap up article for the prerequisites of ML modeling. The Median can be computed as the following:Step 1:… Read the full blog for free on Medium.

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AWS Machine Learning Blog

MAY 16, 2025

In this post, we share how Amazon Web Services (AWS) is helping Scuderia Ferrari HP develop more accurate pit stop analysis techniques using machine learning (ML). Modernizing through partnership with AWS The partnership with AWS is helping Scuderia Ferrari HP modernize the challenging process of pit stop analysis, by using the cloud and ML.

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AWS Machine Learning Blog

MAY 20, 2025

The company also had to manage inconsistent handwritten entries and the need to verify notarization and legal sealstasks that traditional optical character recognition (OCR) and AI and machine learning (AI/ML) solutions struggled to handle effectively.

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AWS Machine Learning Blog

APRIL 8, 2025

With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services such as Amazon SageMaker to efficiently create and deploy ML models. In this post, we show how iFood uses SageMaker to revolutionize its ML operations.

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AWS Machine Learning Blog

APRIL 29, 2025

As AWS LLM League events began rolling out in North America, this initiative represented a strategic milestone in democratizing machine learning (ML) and enabling partners to build practical generative AI solutions for their customers. SageMaker JumpStart is an ML hub that can help you accelerate your ML journey.

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AWS Machine Learning Blog

APRIL 4, 2025

They use real-time data and machine learning (ML) to offer customized loans that fuel sustainable growth and solve the challenges of accessing capital. This approach combines the efficiency of machine learning with human judgment in the following way: The ML model processes and classifies transactions rapidly.

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