AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Data Storage and Processing: All compute is done as Spark jobs inside of a Hadoop cluster using Apache Livy and Spark. Apache HBase was employed to offer real-time key-based access to data.

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AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Our commitment to innovation led us to a pivotal challenge: how to harness the power of machine learning (ML) to further enhance our competitive edge while balancing this technological advancement with strict data security requirements and the need to streamline access to our existing internal resources.

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Hacker News

JUNE 12, 2024

Meta is currently operating many data centers with GPU training clusters across the world. Meta’s training infrastructure comprises dozens of AI clusters of varying sizes, with a plan to scale to 600,000 GPUs in the next year. It runs thousands of training jobs every day from hundreds of different Meta teams.

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AWS Machine Learning Blog

APRIL 19, 2024

The architecture deploys a simple service in a Kubernetes pod within an EKS cluster. The Kubernetes Event Driven Autoscaler ( KEDA ) is configured to automatically scale the number of service pods, based on the custom metrics available in Prometheus. xlarge nodes is included to run system pods that are needed by the cluster.

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Pickl AI

MAY 10, 2023

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. What is Classification?

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

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AWS Machine Learning Blog

APRIL 5, 2024

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Geobox enables city departments to do the following: Improved climate adaptation planning – Informed decisions reduce the impact of extreme heat events.

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AWS Machine Learning Blog

JANUARY 17, 2024

It can represent a geographical area as a whole or it can represent an event associated with a geographical area. We then discuss the various use cases and explore how you can use AWS services to clean the data, how machine learning (ML) can aid in this effort, and how you can make ethical use of the data in generating visuals and insights.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

This capability allows for the seamless addition of SageMaker HyperPod managed compute to EKS clusters, using automated node and job resiliency features for foundation model (FM) development. FMs are typically trained on large-scale compute clusters with hundreds or thousands of accelerators.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

The listing writer microservice publishes listing change events to an Amazon Simple Notification Service (Amazon SNS) topic, which an Amazon Simple Queue Service (Amazon SQS) queue subscribes to. The cluster comprises 3 cluster manager nodes (m6g.xlarge.search instance) dedicated to manage cluster operations.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. Best practices for ml model packaging Here is how you can package a model efficiently.

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AWS Machine Learning Blog

AUGUST 8, 2023

You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you can choose Apache Spark or Ray to easily process large datasets, without worrying about cluster management.

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ODSC - Open Data Science

MAY 31, 2023

The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem. Consumers read the events and process the data in real-time. Editor’s note: Kai Waehner is a speaker for ODSC Europe this June.

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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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AWS Machine Learning Blog

AUGUST 8, 2024

Webex by Cisco is a leading provider of cloud-based collaboration solutions, including video meetings, calling, messaging, events, polling, asynchronous video, and customer experience solutions like contact center and purpose-built collaboration devices. The following diagram illustrates the WxAI architecture on AWS.

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AWS Machine Learning Blog

JANUARY 9, 2023

Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Deploying ML models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Design patterns for building ML applications.

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AWS Machine Learning Blog

DECEMBER 18, 2023

Amazon SageMaker Canvas Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. Through Atlas Data Federation, data is extracted into Amazon S3 bucket.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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Pickl AI

FEBRUARY 17, 2025

It uses predictive modelling to forecast future events and adaptiveness to improve with new data, plus generalization to analyse fresh data. This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

Thomson Reuters , a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. In order to provision a highly scalable cluster that is resilient to hardware failures, Thomson Reuters turned to Amazon SageMaker HyperPod.

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Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Solution overview The following diagram illustrates the ML platform architecture.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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IBM Journey to AI blog

NOVEMBER 13, 2023

Nodes run the pods and are usually grouped in a Kubernetes cluster, abstracting the underlying physical hardware resources. AI and machine learning Building and deploying artificial intelligence (AI) and machine learning (ML) systems requires huge volumes of data and complex processes like high performance computing and big data analysis.

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ODSC - Open Data Science

JUNE 6, 2023

Historically, our space has perceived streaming as a complex technology reserved for experienced data engineers with a deep understanding of incremental event processing. When combined with event-time windows, analyzing the embeddings in real-time becomes much more feasible. October 2022).

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. SageMaker Training is a managed batch ML compute service that reduces the time and cost to train and tune models at scale without the need to manage infrastructure. SageMaker-managed clusters of ml.p4d.24xlarge

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Mlearning.ai

APRIL 15, 2023

How Clustering Can Help You Understand Your Customers Better Customer segmentation is crucial for businesses to better understand their customers, target marketing efforts, and improve satisfaction. Clustering, a popular machine learning technique, identifies patterns in large datasets to group similar customers and gain insights.

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Snorkel AI

MAY 3, 2023

Table of contents Why we needed to redesign our interactive ML system In this section, we’ll go over the market forces and technological shifts that compelled us to re-architect our ML system. Customers tackle high cardinality and multi-label ML problems, requiring far more training data to cover rare classes.

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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AWS Machine Learning Blog

MAY 16, 2024

Event-based pipeline automation After the preprocessing batch was complete and the training/test data was stored in Amazon S3, this event invoked CodeBuild and ran the training pipeline in SageMaker. xlarge","Name":"Master Instance Group"},{"InstanceCount":2,"InstanceGroupType":"CORE","InstanceType":"r5.xlarge","Name":"Core

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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AWS Machine Learning Blog

OCTOBER 2, 2024

This approach, when applied to generative AI solutions, means that a specific AI or machine learning (ML) platform configuration can be used to holistically address the operational excellence challenges across the enterprise, allowing the developers of the generative AI solution to focus on business value.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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