AWS Machine Learning Blog

APRIL 24, 2024

The solution uses Amazon SageMaker JumpStart as the core service for model deployment, fine-tuning, and reinforcement learning. In the pipeline, we used Amazon OpenSearch Service for the vector database, and deployed a fine-tuned Mistral-7B-Instruct model on Amazon SageMaker. Answer the score inside of tags.

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AWS Machine Learning Blog

AUGUST 4, 2023

This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. Use the newly launched capability of the Amazon SageMaker Data Wrangler connector for Salesforce Data Cloud to prepare the data in SageMaker without copying the data from Salesforce Data Cloud.

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AWS Machine Learning Blog

JULY 25, 2023

Amazon SageMaker JumpStart is an ML hub offering algorithms, models, and ML solutions. With SageMaker JumpStart, ML practitioners can choose from a growing list of best performing open source FMs. For the complete list, check out Getting started with Amazon SageMaker JumpStart.

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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

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Add any optional tags, then choose Next.

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AWS Machine Learning Blog

MAY 25, 2023

To understand the overall structure of a RAG-based approach, refer to Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart. We then demonstrate how to use LangChain for tying everything together: Interfacing with LLMs hosted on Amazon SageMaker.

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AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Amazon SageMaker JumpStart provides one-click, end-to-end solutions for many common ML use cases. JumpStart solution templates.

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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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Mlearning.ai

FEBRUARY 6, 2023

Use SageMaker Processing Jobs to easily run inference on your large Dataset with Hugging Face’s Transformer Models Photo by Alex Kulikov on Unsplash This blog will give you a complete walk through of running a distributed batch inference on large data in production. We’ll be using Amazon Sagemaker, a fully managed machine learning service.

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Smart Data Collective

APRIL 5, 2022

They are using tools like Amazon SageMaker to take advantage of more powerful machine learning capabilities. Amazon SageMaker is a hardware accelerator platform that uses cloud-based machine learning technology. Although it is primarily cloud-based, SageMaker also works on embedded systems as well. Google Cloud AI Platform.

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AWS Machine Learning Blog

MARCH 6, 2024

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. We start by downloading a public dataset using Amazon SageMaker Studio. The following diagram illustrates this workflow. apply(lambda x: len(x)).between(100,

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DagsHub

SEPTEMBER 7, 2023

By using AWS SageMaker for customizable pipeline solutions triggered by code base modifications and DagsHub for data and code management and experiment tracking - building and managing those pipelines becomes a manageable task. How to setup SageMaker for Continuous Training?

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AWS Machine Learning Blog

APRIL 11, 2023

With the integrated development environment (IDE) of Amazon SageMaker Studio with Jupyter Lab 3 , we can build, run, and serve Streamlit web apps from within that same environment for development purposes. Alternatively, you can use the SageMaker console to change the domain settings. Create Studio using JupyterLab 3.0

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AWS Machine Learning Blog

MAY 5, 2023

In this post, we show how to repurpose an existing dataset via data cleaning, preprocessing, and pre-labeling with a zero-shot classification model in FiftyOne , and adjusting these labels with Amazon SageMaker Ground Truth. You can use Ground Truth and FiftyOne to accelerate your data labeling project.

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AWS Machine Learning Blog

APRIL 23, 2024

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. To use Local Mode, set instance_type='local' when running SageMaker Python SDK jobs such as training and inference.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

We have provided a Jupyter notebook that creates all the Amazon Personalize resources, taking advantage of the fully managed Jupyter notebook instance capabilities of Amazon SageMaker. Deploy the CloudFormation stack The CloudFormation stack automates the deployment of the OpenSearch Service domain and SageMaker Notebook instance.

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AWS Machine Learning Blog

AUGUST 2, 2023

In this post, we show how to use CDE in Amazon Kendra using a GenAI model deployed on Amazon SageMaker. It allows users to quickly and easily find the images they need without having to manually tag or categorize them. This newly trained model can then be deployed in SageMaker for the image captioning described in this solution.

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AWS Machine Learning Blog

OCTOBER 6, 2023

We also demonstrate how to deploy these pre-trained models on Amazon SageMaker. Furthermore, we discuss the diverse applications of these models, focusing particularly on several real-world scenarios, such as zero-shot tag and attribution generation for ecommerce and automatic prompt generation from images.

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AWS Machine Learning Blog

NOVEMBER 20, 2023

KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. The AI Food Tag can help patients with chronic diseases such as diabetes manage their diets. In this post, we describe KT’s model development journey and success using SageMaker.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. Deutsche Bahn has been at the forefront in adopting AI, using SageMaker Studio as a key AI platform. AI’s growing influence in large organizations brings crucial challenges in managing AI platforms.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

We propose using this capability with the Amazon SageMaker platform of services to improve regression model accuracy in an ML use case, and independently, for the automated tagging of visual images. SageMaker Data Wrangler is purpose-built to simplify the process of data preparation and feature engineering.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

We are excited to share that Amazon SageMaker HyperPod is now generally available to enable training foundation models with thousands of accelerators up to 40% faster by providing a highly resilient training environment while eliminating the undifferentiated heavy lifting involved in operating large-scale training clusters.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

In this post, we walk through best practices for managing LoRA fine-tuned models on Amazon SageMaker to address this emerging question. Working with FMs on SageMaker Model Registry In this post, we walk through an end-to-end example of fine-tuning the Llama2 large language model (LLM) using the QLoRA method.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio.

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AWS Machine Learning Blog

OCTOBER 2, 2023

However, using purpose-built services like Amazon SageMaker and AWS IoT Greengrass allows you to significantly reduce this effort. If you’re just getting started with MLOps at the edge on AWS, refer to MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass for an overview and reference architecture.

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AWS Machine Learning Blog

MARCH 15, 2023

The term legacy code refers to code that was developed to be manually run on a local desktop, and is not built with cloud-ready SDKs such as the AWS SDK for Python (Boto3) or Amazon SageMaker Python SDK. 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 2, 2023

The sample use case used for this series is a visual quality inspection solution that can detect defects on metal tags, which you can deploy as part of a manufacturing process. In a production setting, you can decide to automatically notify another system that takes care of removing faulty metal tags from the production line.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. Both JupyterLab and Code Editor can be launched using a flexible workspace called Spaces.

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AWS Machine Learning Blog

OCTOBER 20, 2023

We hear from customers that they require specialized knowledge and investment of up to 12 months for building out their customized Amazon SageMaker ML platform implementation to ensure scalable, reliable, secure, and governed ML environments for their lines of business (LOBs) or ML teams.

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AWS Machine Learning Blog

MARCH 15, 2024

In addition to Amazon Bedrock, you can use other AWS services like Amazon SageMaker JumpStart and Amazon Lex to create fully automated and easily adaptable generative AI order processing agents. Furthermore, these libraries are required when calling the Amazon Bedrock API from Amazon SageMaker Studio. awscli>=1.29.57

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Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Finally, we show how to export the data flow and train a model using SageMaker Autopilot. Access to Amazon SageMaker , an instance of Amazon SageMaker Studio , and a user for Studio.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. SageMaker makes it easy to deploy models into production directly through API calls to the service. SageMaker provides a variety of options to deploy models.

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AWS Machine Learning Blog

JUNE 27, 2023

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications.

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AWS Machine Learning Blog

AUGUST 15, 2023

In this post, we demonstrate how to use the power of RAG in building a Q&A solution for video and audio assets on Amazon SageMaker. Build a multi-functional chatbot using LLMs with SageMaker, where the two aforementioned solutions are wrapped and deployed. For hosting Falcon-40B, we use an ml.g5.12xlarge SageMaker hosting instance.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. SageMaker Data Wrangler supports fine-grained data access control with Lake Formation and Amazon Athena connections. The EMR cluster should be created with encryption in transit.

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AWS Machine Learning Blog

JULY 3, 2023

In computer vision (CV), adding tags to identify objects of interest or bounding boxes to locate the objects is called labeling. Amazon SageMaker Ground Truth – Alternatively, you can manage your own data labeling workflows and workforce to label data. For a full list of features, refer to Amazon SageMaker Data Labeling Features.

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AWS Machine Learning Blog

APRIL 25, 2023

Amazon SageMaker Model Training helps data scientists run fully managed large-scale training jobs on AWS’s compute infrastructure. SageMaker Training also helps data scientists with advanced tools such as Amazon SageMaker Debugger and Profiler to debug and analyze their large-scale training jobs.

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