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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Hacker News

FEBRUARY 6, 2024

Cluster Crashes, Battling Complexity, Scaling, Power Of Helm, Tracing & Observability, From Self-Managed On AWS To Managed On AKS, And More

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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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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

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2 billion in Pythia.

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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

MAY 23, 2023

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. Amazon Bedrock also offers access to Titan foundation models, a family of models trained in-house by AWS. Deploy the AWS CDK application.

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IBM Journey to AI blog

APRIL 3, 2024

In this blog post, we walk through the recommended options for running IBM TAS on Amazon Web Services (AWS). We discuss the architecture and describe how the IBM, Red Hat ® and AWS components come together and provide a solid foundation for running IBM TAS.

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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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Smart Data Collective

NOVEMBER 7, 2022

One of the best known options is Amazon Web Services (AWS). What is Amazon Web Services (AWS)? AWS is a collection of remote computing services (or web services). AWS Cloud is a suite of hosting products used by such services as Dropbox, Reddit, and others. AWS is a cloud computing service. AWS Lambda.

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Smart Data Collective

FEBRUARY 20, 2022

AWS (Amazon Web Services), the comprehensive and evolving cloud computing platform provided by Amazon, is comprised of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS). With its wide array of tools and convenience, AWS has already become a popular choice for many SaaS companies.

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databricks

DECEMBER 14, 2022

At Databricks, we run our compute infrastructure on AWS, Azure, and GCP. We orchestrate containerized services using Kubernetes clusters. We develop and manage.

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Twilio Segment

APRIL 5, 2022

We detail how to host Fleet on an EKS cluster and send scheduled query logs to an AWS Opensource destination entirely created and managed as code.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

In this post, you’ll see an example of performing drift detection on embedding vectors using a clustering technique with large language models (LLMS) deployed from Amazon SageMaker JumpStart. Then we use K-Means to identify a set of cluster centers. A visual representation of the silhouette score can be seen in the following figure.

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phData

APRIL 26, 2024

If you don’t have a Spark environment set up in your Cloudera environment, you can easily set up a Dataproc cluster on Google Cloud Platform (GCP) or an EMR cluster on AWS to do hands-on on your own. Create a Dataproc Cluster: Click on Navigation Menu > Dataproc > Clusters. Click Create Cluster.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

When AWS launched purpose-built accelerators with the first release of AWS Inferentia in 2020, the M5 team quickly began to utilize them to more efficiently deploy production workloads , saving both cost and reducing latency. We use AWS Batch automated retries to retry jobs that encounter a transient failure during model training.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

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AWS Machine Learning Blog

MARCH 30, 2023

Metadata of the match is processed within the AWS Lambda function MetaDataIngestion , while positional data is ingested using the AWS Fargate container called MatchLink. Additionally, the ball recovery times are sent to a specific topic in the MSK cluster, where they can be accessed by other Bundesliga Match Facts.

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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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IBM Journey to AI blog

MARCH 27, 2024

However, this leads to skyrocketing cloud costs due to inefficient data processing and the need for resource-consuming cluster solutions. EclipseStore enables data storage by synchronizing any Java object graph of any size and complexity seamlessly with any binary data storage such as AWS S3 or IBM Cloud® Object Storage.

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AWS Machine Learning Blog

APRIL 17, 2023

Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing. In this post, we demonstrate how you can connect your RStudio on SageMaker domain with an EMR cluster. Choose Create stack.

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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

APRIL 18, 2024

It’s straightforward to deploy in your AWS account. Prerequisites You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. Everything you need is provided as open source in our GitHub repo.

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Data Science 101

NOVEMBER 11, 2019

If you are at a University or non-profit, you can ask for cash and/or AWS credits. AWS Parallel Cluster for Machine Learning AWS Parallel Cluster is an open-source cluster management tool. It can be used to do distributed Machine Learning on AWS. Google Cloud.

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Smart Data Collective

MARCH 2, 2022

Kubernetes is based on a control node combined with multiple worker nodes to facilitate its cluster architecture. With the emergence of serverless technologies, there is growing interest in utilizing serverless within Kubernetes both to manage workloads and provide the cluster itself. Deploying Serverless Workloads in Kubernetes.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

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IBM Journey to AI blog

FEBRUARY 15, 2024

Autoscaling When traffic spikes, Kubernetes can automatically spin up new clusters to handle the additional workload. However, unlike VMs, Kubernetes orchestrates container interactions that transcend apps and clusters. This includes data in CI/CD pipelines (which feed into K8s clusters) and GitOps workflows (which power K8s clusters).

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AWS Machine Learning Blog

APRIL 17, 2024

Despite the availability of advanced distributed training libraries, it’s common for training and inference jobs to need hundreds of accelerators (GPUs or purpose-built ML chips such as AWS Trainium and AWS Inferentia ), and therefore tens or hundreds of instances. or later NPM version 10.0.0

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Model training was accelerated by 50% through the use of the SMDDP library, which includes optimized communication algorithms designed specifically for AWS infrastructure. For SageMaker distributed training, the instances need to be in the same AWS Region and Availability Zone. days in AWS vs. 9 days on their legacy platform).

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AWS Machine Learning Blog

DECEMBER 29, 2022

AWS offers tools such as RStudio on SageMaker and Amazon Redshift to help tackle these challenges. Note: If you already have an RStudio domain and Amazon Redshift cluster you can skip this step. Amazon Redshift Serverless cluster. I acknowledge that AWS CloudFormation might create IAM resources with custom names.

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Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. Amazon’s AWS Glue is one such tool that allows you to consume data from Apache Kafka and Amazon-managed streaming for Apache Kafka (MSK).

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AWS Machine Learning Blog

DECEMBER 5, 2023

Walkthrough AWS-optimized AllGather AWS-optimized AllGather uses the following techniques to achieve better performance on AWS infrastructure compared to NCCL: We move data between instances via Elastic Fabric Adapter (EFA) network with an all-to-all communication pattern. 24xlarge nodes (512 NVIDIA A100 GPUs) PyTorch FSDP 97.89

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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

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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Julien Simon

JANUARY 2, 2023

In this video, you will learn how to accelerate a PyTorch training job with a cluster of Intel Sapphire Rapids servers running on AWS. We will use the Intel oneAPI Collective Communications Library (CCL) to distribute the job, and the Intel Extension for PyTorch (IPEX) library to automatically put the new CPU instructions to work.

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AWS Machine Learning Blog

MAY 3, 2023

AWS customer Vericast is a marketing solutions company that makes data-driven decisions to boost marketing ROIs for its clients. Dynamic scaling of feature engineering jobs – A combination of various AWS services is used for this, but most notably SageMaker Processing.

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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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