AWS Machine Learning Blog

JUNE 25, 2024

Amazon Web Services is excited to announce the launch of the AWS Neuron Monitor container , an innovative tool designed to enhance the monitoring capabilities of AWS Inferentia and AWS Trainium chips on Amazon Elastic Kubernetes Service (Amazon EKS). The Container Insights dashboard also shows cluster status and alarms.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. Third, we’ll explore the robust infrastructure services from AWS powering AI innovation, featuring Amazon SageMaker , AWS Trainium , and AWS Inferentia under AI/ML, as well as Compute topics.

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AWS Machine Learning Blog

MAY 23, 2023

For information about how to use JumpStart models programmatically, see Use SageMaker JumpStart Algorithms with Pretrained Models. In April 2023, AWS unveiled Amazon Bedrock , which provides a way to build generative AI-powered apps via pre-trained models from startups including AI21 Labs , Anthropic , and Stability AI.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.

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AWS Machine Learning Blog

APRIL 7, 2023

To reduce the barrier to entry of ML at the edge, we wanted to demonstrate an example of deploying a pre-trained model from Amazon SageMaker to AWS Wavelength , all in less than 100 lines of code. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Prerequisites Before you begin, make sure you have the following prerequisites in place: An AWS account and role with the AWS Identity and Access Management (IAM) privileges to deploy the following resources: IAM roles. For this post we’ll use a provisioned Amazon Redshift cluster. A SageMaker domain. Database name : Enter dev.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

During the iterative research and development phase, data scientists and researchers need to run multiple experiments with different versions of algorithms and scale to larger models. However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model. An AutoML tool applies a combination of different algorithms and various preprocessing techniques to your data. The following diagram presents the overall solution workflow.

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AWS Machine Learning Blog

JANUARY 17, 2024

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. For more information, refer to Common techniques to detect PHI and PII data using AWS Services.

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AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB’s robust time series data management allows for the storage and retrieval of large volumes of time-series data in real-time, while advanced machine learning algorithms and predictive capabilities provide accurate and dynamic forecasting models with SageMaker Canvas. Setup the Database access and Network access.

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AWS Machine Learning Blog

DECEMBER 22, 2023

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Integrating tensor parallelism to enable training on massive clusters This release of SMP also expands PyTorch FSDP’s capabilities to include tensor parallelism techniques.

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Data Science Dojo

JULY 24, 2023

Different algorithms and techniques are employed to achieve eventual consistency. Clusters : Clusters are groups of interconnected nodes that work together to process and store data. Clusters : Clusters are groups of interconnected nodes that work together to process and store data.

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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. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. In the first post , we described FL concepts and the FedML framework.

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AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. Recommendation model using NCF NCF is an algorithm based on a paper presented at the International World Wide Web Conference in 2017.

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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. Model training was accelerated by 50% through the use of the SMDDP library, which includes optimized communication algorithms designed specifically for AWS infrastructure.

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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. In the past few years, numerous customers have been using the AWS Cloud for LLM training. We recommend working with your AWS account team or contacting AWS Sales to determine the appropriate Region for your LLM workload.

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AWS Machine Learning Blog

DECEMBER 13, 2024

The approach uses three sequential BERTopic models to generate the final clustering in a hierarchical method. Clustering We use the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) method to form different use case clusters. Lastly, a third layer is used for some of the clusters to create sub-topics.

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ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

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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. One of the several challenges faced was adapting the existing on-premises pipeline solution for use on AWS.

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AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

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AWS Machine Learning Blog

MARCH 4, 2024

The new service achieved a 4-6 times improvement in topic assertion by tightly clustering on several dozen key topics vs. hundreds of noisy NLP keywords. NLP vs. LLM Alida’s existing NLP solution relies on clustering algorithms and statistical classification to analyze open-ended survey responses.

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AWS Machine Learning Blog

JANUARY 6, 2025

It usually comprises parsing log data into vectors or machine-understandable tokens, which you can then use to train custom machine learning (ML) algorithms for determining anomalies. You can adjust the inputs or hyperparameters for an ML algorithm to obtain a combination that yields the best-performing model. scikit-learn==0.21.3

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. It also means not all workloads are equally suitable for acceleration.

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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 19, 2024

We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution.

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AWS Machine Learning Blog

MARCH 2, 2023

In our solution, we implement a hyperparameter grid search on an EKS cluster for tuning a bert-base-cased model for classifying positive or negative sentiment for stock market data headlines. A desired cluster can simply be configured using the eks.conf file and launched by running the eks-create.sh to launch the cluster.

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AWS Machine Learning Blog

NOVEMBER 3, 2023

To achieve this, our process uses a synchronization algorithm that is trained on a labeled dataset. This algorithm robustly associates each shot with its corresponding tracking data. Shot speed calculation The heart of determining shot speed lies in a precise timestamp given by our synchronization algorithm.

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AWS Machine Learning Blog

JUNE 7, 2023

Starting June 7th, both Falcon LLMs will also be available in Amazon SageMaker JumpStart, SageMaker’s machine learning (ML) hub that offers pre-trained models, built-in algorithms, and pre-built solution templates to help you quickly get started with ML. The model weights are available to download, inspect and deploy anywhere.

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AWS Machine Learning Blog

MAY 3, 2023

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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Flipboard

MARCH 13, 2023

Therefore, VMware Carbon Black and AWS chose to build a custom MLOps pipeline using Amazon SageMaker for its ease of use, versatility, and fully managed infrastructure. In this post, VMware Carbon Black and AWS architects discuss how we built and managed custom ML workflows using Gitlab , Amazon MWAA, and SageMaker.

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AWS Machine Learning Blog

JUNE 6, 2024

You can deploy this model with SageMaker JumpStart, a machine learning (ML) hub with foundation models, built-in algorithms, and pre-built ML solutions that you can deploy with just a few clicks. This makes the models especially powerful at tasks such as clustering for long documents like legal text or product documentation.

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AWS Machine Learning Blog

JULY 6, 2023

A number of AWS independent software vendor (ISV) partners have already built integrations for users of their software as a service (SaaS) platforms to utilize SageMaker and its various features, including training, deployment, and the model registry. In some cases, an ISV may deploy their software in the customer AWS account.

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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

OCTOBER 23, 2023

The performance of Talent.com’s matching algorithm is paramount to the success of the business and a key contributor to their users’ experience. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com.

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AWS Machine Learning Blog

JANUARY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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AWS Machine Learning Blog

MAY 30, 2023

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. With SageMaker training jobs, you can bring your own algorithm or choose from more than 25 built-in algorithms. You can further use CloudWatch custom algorithm metrics to monitor the training performance.

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AWS Machine Learning Blog

MAY 25, 2023

We use a combination of different AWS services, open-source foundation models ( FLAN-T5 XXL for text generation and GPT-j-6B for embeddings) and packages such as LangChain for interfacing with all the components and Streamlit for building the bot frontend. AWS Identity and Access Management roles and policies for access management.

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