AWS Machine Learning Blog

MAY 31, 2023

With containers, scaling on a cluster becomes much easier. In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium accelerators, which are purpose built for high-performance deep learning training. What you get is an ML development environment that is consistent and portable.

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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 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

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AWS Machine Learning Blog

MARCH 15, 2023

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK.

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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 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Prototyping developed an AWS Cloud Development Kit (AWS CDK) stack for deployment following AWS best practices.

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AWS Machine Learning Blog

MAY 23, 2023

You can also access JumpStart models using the SageMaker Python SDK. 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. Clone and set up the AWS CDK application.

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AWS Machine Learning Blog

DECEMBER 24, 2024

The process of setting up and configuring a distributed training environment can be complex, requiring expertise in server management, cluster configuration, networking and distributed computing. To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023.

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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

SEPTEMBER 3, 2024

Cost optimization – The serverless nature of the integration means you only pay for the compute resources you use, rather than having to provision and maintain a persistent cluster. SageMaker Studio runs inside an AWS managed virtual private cloud ( VPC ), with network access for SageMaker Studio domains, in this setup configured as VPC-only.

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Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. The following diagram shows an example.

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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

SEPTEMBER 14, 2023

With Amazon SageMaker , now you can run a SageMaker training job simply by annotating your Python code with @remote decorator. The SageMaker Python SDK automatically translates your existing workspace environment, and any associated data processing code and datasets, into an SageMaker training job that runs on the training platform.

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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

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Solution overview Amazon Transcribe is the go-to service for speaker diarization in AWS. PyAnnote is an open source toolkit written in Python for speaker diarization.

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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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Data Science Dojo

JULY 24, 2023

Clusters : Clusters are groups of interconnected nodes that work together to process and store data. Clustering allows for improved performance and fault tolerance as tasks can be distributed across nodes. Amazon S3: Amazon Simple Storage Service (S3) is a scalable object storage service provided by Amazon Web Services (AWS).

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PyImageSearch

SEPTEMBER 30, 2024

Home Table of Contents Introduction to GitHub Actions for Python Projects Introduction What Is CICD? For Python projects, CI/CD pipelines ensure that your code is consistently integrated and delivered with high quality and reliability. Git is the most commonly used VCS for Python projects, enabling collaboration and version tracking.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. Clusters are provisioned with the instance type and count of your choice and can be retained across workloads. As a result of this flexibility, you can adapt to various scenarios.

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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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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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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 18, 2024

We demonstrate this solution by walking you through a comprehensive step-by-step guide on how to fine-tune YOLOv8 , a real-time object detection model, on Amazon Web Services (AWS) using a custom dataset. You can train foundation models (FMs) for weeks and months without disruption by automatically monitoring and repairing training clusters.

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AWS Machine Learning Blog

APRIL 24, 2024

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. It is hosted on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate , and it is accessed using an Application Load Balancer. It serves as the data source to the knowledge base.

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AWS Machine Learning Blog

MAY 16, 2024

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Make sure to enter the same PyTorch framework, Python version, and other details that you used to train the model. Provide the inference.py

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. The full code can be found on the aws-samples-for-ray GitHub repository. XGBoost-Ray uses this information to distribute the training across all the nodes attached to the Ray cluster.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. or later image versions. or later image versions.

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Data Science Dojo

JULY 6, 2023

It supports various data types and offers advanced features like data sharing and multi-cluster warehouses. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is known for its high performance and cost-effectiveness.

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AWS Machine Learning Blog

DECEMBER 18, 2024

Training an LLM is a compute-intensive and complex process, which is why Fastweb, as a first step in their AI journey, used AWS generative AI and machine learning (ML) services such as Amazon SageMaker HyperPod. The team opted for fine-tuning on AWS.

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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. GPU PBAs, 4% other PBAs, 4% FPGA, and 0.5%

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Generate and run data transformation Python code. Stream 3: Generate and run data transformation Python code Next, we took the response from the API call and transformed it to answer the user question. The request arrives at the microservice on our existing Amazon Elastic Container Service (Amazon ECS) cluster.

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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

DECEMBER 13, 2024

This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows through SageMaker Studio.

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AWS Machine Learning Blog

JANUARY 6, 2023

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. Background. Prerequisites.

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AWS Machine Learning Blog

JUNE 6, 2024

This makes the models especially powerful at tasks such as clustering for long documents like legal text or product documentation. With SageMaker JumpStart, the model is deployed in an AWS secure environment and under your VPC controls, helping provide data security. Choose Deploy.

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AWS Machine Learning Blog

OCTOBER 16, 2023

Infrastructure and development challenges Veriff’s backend architecture is based on a microservices pattern, with services running on different Kubernetes clusters hosted on AWS infrastructure. For more information, refer to Managing Python Runtime and Libraries. Solutions Architect at AWS based in Helsinki, Finland.

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