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 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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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

APRIL 19, 2024

We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. They are also supported by AWS CloudFormation. Release v1.2.9

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AWS Machine Learning Blog

OCTOBER 23, 2024

The following demo shows Agent Creator in action. SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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AWS Machine Learning Blog

OCTOBER 22, 2024

In this post, we explore how organizations can address these challenges and cost-effectively customize and adapt FMs using AWS managed services such as Amazon SageMaker training jobs and Amazon SageMaker HyperPod. After the training is complete, SageMaker spins down the cluster and the customer is billed for the net training time in seconds.

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AWS Machine Learning Blog

APRIL 18, 2024

Check out the following demo to see how it works. 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.

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AWS Machine Learning Blog

JANUARY 31, 2024

Amazon Titan Text Embeddings is a text embeddings model that converts natural language text—consisting of single words, phrases, or even large documents—into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity. Nitin Eusebius is a Sr.

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AWS Machine Learning Blog

DECEMBER 15, 2023

Prerequisites To implement this solution, complete the following prerequisites: Have AWS Cloud admin access with an AWS Identity and Access Management (IAM) user with permissions required to complete the integration. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster.

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Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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AWS Machine Learning Blog

APRIL 5, 2023

You can also use an AWS CloudFormation template by following the GitHub instructions to create a domain. By using an interface VPC endpoint (interface endpoint), the communication between your VPC and Studio is conducted entirely and securely within the AWS network. For demo purposes, we use approximately 1,600 products.

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AWS Machine Learning Blog

AUGUST 24, 2023

Today, we’re pleased to announce the preview of Amazon SageMaker Profiler , a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. The following table provides the links to the supported AWS Deep Learning Containers for SageMaker.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

AWS is uniquely positioned to help you address these challenges through generative AI, with a broad and deep range of AI/ML services and over 20 years of experience in developing AI/ML technologies. Under Connect Amazon Q to IAM Identity Center , choose Create account instance to create a custom credential set for this demo.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

The demo implementation code is available in the following GitHub repo. Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). With SageMaker, you can seamlessly deploy IDEFICS-9b-instruct on a g5.2xlarge instance for inference tasks.

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AWS Machine Learning Blog

AUGUST 22, 2023

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. Each account or Region has its own training instances.

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AWS Machine Learning Blog

MARCH 19, 2024

For multiple-choice reasoning, we prompt AI21 Labs Jurassic-2 Mid on a small sample of questions from the AWS Certified Solutions Architect – Associate exam. Prerequisites This walkthrough assumes the following prerequisites: An AWS account with a ml.t3.medium We use Cohere Command and AI21 Labs Jurassic-2 Mid for this demo.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Generative AI on AWS can transform user experiences for customers while maintaining brand consistency and your desired customization. Here, we also prompted the LLM to use the company logo (which is the unicorn of AWS GameDay ) to demonstrate incorporating existing design elements into the design. The AWS SDK for Python (Boto3) set up.

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AWS Machine Learning Blog

APRIL 19, 2023

Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. It also includes support for new hardware like ARM (both in servers like AWS Graviton and laptops with Apple M1 ) and AWS Inferentia.

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DataRobot

JUNE 16, 2021

The MLOps Management Agent provides a framework to automate the entire model deployment lifecycle in any environment or infrastructure such as Azure, GCP, AWS, or your own on-premise Kubernetes cluster. What’s the new page , you can find a demo video to see how it works. Request a Demo. It is available in Release 7.1.

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AWS Machine Learning Blog

JANUARY 17, 2023

To try out the solution in your own account, make sure that you have the following in place: An AWS account. To run this JumpStart solution and have the infrastructure deploy to your AWS account, you must create an active Amazon SageMaker Studio instance (see Onboard to Amazon SageMaker Studio ). Demo notebook. Conclusion.

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AWS Machine Learning Blog

MAY 2, 2023

Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available. In this demo, we use a Jumpstart Flan T5 XXL model endpoint. Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available. SageMaker Savings Plans apply only to SageMaker ML Instance usage.

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AWS Machine Learning Blog

MAY 24, 2023

You can efficiently deploy the pre-trained J2-jumbo-instruct, or other Jurassic-2 models available on AWS Marketplace, into your own own virtual private cloud (VPC) using Amazon SageMaker. 24xlarge") # Create a Sgaemkaer endpoint then deploy a pre-trained J2-jumbo-instruct-v1 model from AWS Market Place.

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Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. You can register for a live demo of Snorkel Flow on February 16 which will feature the platform’s new FM capabilities.

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Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. You can register for a live demo of Snorkel Flow on February 16 which will feature the platform’s new FM capabilities.

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AWS Machine Learning Blog

APRIL 3, 2023

We cover prompts for the following NLP tasks: Text summarization Common sense reasoning Question answering Sentiment classification Translation Pronoun resolution Text generation based on article Imaginary article based on title Code for all the steps in this demo is available in the following notebook.

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O'Reilly Media

NOVEMBER 28, 2023

How will AI adopters react when the cost of renting infrastructure from AWS, Microsoft, or Google rises? Second, while OpenAI’s GPT-4 announcement last March demoed generating website code from a hand-drawn sketch, that capability wasn’t available until after the survey closed. But they may back off on AI development.

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Pickl AI

APRIL 26, 2024

Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. Have you worked with cloud-based data platforms like AWS, Google Cloud, or Azure? Additional Benefits Free demo sessions. What approach would you take?

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AWS Machine Learning Blog

APRIL 18, 2023

All the steps in this demo are available in the accompanying notebook Fine-tuning text generation GPT-J 6B model on a domain specific dataset. We serve developers and enterprises of all sizes through AWS, which offers a broad set of global compute, storage, database, and other service offerings.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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The MLOps Blog

DECEMBER 29, 2022

A GPU machine on GCP, or AWS has a CPU on it. How do you look at an On-Premises GPU cluster, managed by NVIDIA AI enterprise software suite in combination with Red Hat OpenShift or VMware Tanzu, over something like AWS stack or Azure stack for the same GPU cluster managed by EKS, for example?

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AWS Machine Learning Blog

APRIL 18, 2023

All the steps in this demo are available in the accompanying notebook Fine-tuning text generation GPT-J 6B model on a domain specific dataset. We serve developers and enterprises of all sizes through AWS, which offers a broad set of global compute, storage, database, and other service offerings.

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The MLOps Blog

MARCH 21, 2023

We ask this during product demos, user and support calls, and on our MLOps LIVE podcast. Orchestrators are concerned with lower-level abstractions like machines, instances, clusters, service-level grouping, replication, and so on. If your organization runs its workloads on AWS , it might be worth it to leverage AWS SageMaker.

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The MLOps Blog

MAY 9, 2023

3 Quickly build and deploy an end-to-end ML pipeline with Kubeflow Pipelines on AWS. Kubeflow Pipelines Kubeflow Pipelines is an orchestration tool for building and deploying portable, scalable, and reproducible end-to-end machine learning workflows directly on Kubernetes clusters. If you don’t already have an AWS account, create one.

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AWS Machine Learning Blog

AUGUST 14, 2023

The code for all the steps in this demo is available in the following notebook. These attributes are only default values; you can override them and retain granular control over the AWS models you create. LMI is an AWS-built LLM software stack (container) that offers easy-to-use functions and performance gain on generative AI models.

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The MLOps Blog

OCTOBER 3, 2023

For example, you can use BigQuery , AWS , or Azure. It can be a cluster run by Kubernetes or maybe something else. How awful are they?” In terms of the interaction, ideally, the data scientists shouldn’t have to be setting up infrastructure like a Spark cluster. They’re terrible people.

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AWS Machine Learning Blog

JANUARY 28, 2025

On the Add additional capacity page, select Developer edition (for this demo) and choose Next. Under Authentication , if you already have credentials stored in AWS Secrets Manager , choose it on the dropdown Otherwise, choose Create and add new secret. Choose Next. Under User-group expansion , select None.

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AWS Machine Learning Blog

APRIL 1, 2025

The following code imports the necessary libraries, including Boto3 for AWS services, LangChain components, and Streamlit. Clean up When you have finished experimenting with this solution, clean up your resources to prevent AWS charges from being incurred: Empty the S3 buckets. Clone the GitHub repo to make a local copy.

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