Analytics Vidhya

DECEMBER 30, 2022

Introduction In machine learning, the data is an essential part of the training of machine learning algorithms. The amount of data and the data quality highly affect the results from the machine learning algorithms. Almost all machine learning algorithms are data dependent, and […].

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Flipboard

JUNE 20, 2023

For reference, GPT-3, an earlier generation LLM has 175 billion parameters and requires months of non-stop training on a cluster of thousands of accelerated processors. The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators.

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AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. In this post, we seek to separate a time series dataset into individual clusters that exhibit a higher degree of similarity between its data points and reduce noise.

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AWS Machine Learning Blog

OCTOBER 5, 2023

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

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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

JANUARY 30, 2025

Smart Subgroups For a user-specified patient population, the Smart Subgroups feature identifies clusters of patients with similar characteristics (for example, similar prevalence profiles of diagnoses, procedures, and therapies). The AML feature store standardizes variable definitions using scientifically validated algorithms.

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AWS Machine Learning Blog

MARCH 15, 2024

To mitigate these risks, the FL model uses personalized training algorithms and effective masking and parameterization before sharing information with the training coordinator. Therefore, ML creates challenges for AWS customers who need to ensure privacy and security across distributed entities without compromising patient outcomes.

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AWS Machine Learning Blog

JANUARY 30, 2025

You can now use DeepSeek-R1 to build, experiment, and responsibly scale your generative AI ideas on AWS. The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert clusters. 48xlarge instance in the AWS Region you are deploying.

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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. The goal is to index these five webpages dynamically using a common embedding algorithm and then use a retrieval (and reranking) strategy to retrieve chunks of data from the indexed knowledge base to infer the final answer.

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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

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

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

FEBRUARY 5, 2025

Previously, OfferUps search engine was built with Elasticsearch (v7.10) on Amazon Elastic Compute Cloud (Amazon EC2), using a keyword search algorithm to find relevant listings. The listing indexer AWS Lambda function continuously polls the queue and processes incoming listing updates.

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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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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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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

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

JANUARY 31, 2024

This technique is achieved through the use of ML algorithms that enable the understanding of the meaning and context of data (semantic relationships) and the learning of complex relationships and patterns within the data (syntactic relationships). You can use it via either the Amazon Bedrock REST API or the AWS SDK.

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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

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

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

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

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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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

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

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

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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Pickl AI

JANUARY 12, 2025

A generative AI company exemplifies this by offering solutions that enable businesses to streamline operations, personalise customer experiences, and optimise workflows through advanced algorithms. Data forms the backbone of AI systems, feeding into the core input for machine learning algorithms to generate their predictions and insights.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. He focuses on developing scalable machine learning algorithms.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

The integration with Amazon Bedrock is achieved through the Boto3 Python module, which serves as an interface to the AWS, enabling seamless interaction with Amazon Bedrock and the deployment of the classification model. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

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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

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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