Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. - GitHub - aws/amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

This is a collection of various sample projects and Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker for a multitude of Machine Learning use cases.

Using models for identifying non-compliance at a workplace provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace. Extracting insights from your credit card statements provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for efficiently processing financial transaction logs. These Amazon SageMaker examples fully illustrate a concept, but may require some additional configuration on the users part to complete.

git clone https://github.com/aws/amazon-sagemaker-examples.git ... We recommend the following notebooks as a broad introduction to the capabilities that SageMaker offers. To explore in even more depth, we provide additional notebooks covering even more use cases and frameworks.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. - aws/amazon-sagemaker-examples

In this repository we showcase how you can take different SageMaker Inference options such as Real-Time Inference (low latency, high throughput use-cases) and Asynchronous Inference (near real-time/batch use-cases) and integrate with Model Servers ...

This repository is a sibling of official sagemaker examples repository where the official sagemaker examples repository demonstrates only the core capabilities of Amazon SageMaker, while this repository covers additional examples which may go beyond SageMaker or may cover more advanced examples combining multiple features/services of AWS together.

Industry Use Cases: Financial services, healthcare, retail, and manufacturing applications · Enterprise Security: IAM integration, VPC support, encryption at rest and in transit · Amazon SageMaker - Managed ML platform with training, inference, ...

The boto3 reference for SageMaker and the SageMaker API reference: in case you have use cases for SageMaker where you want (or need) to use low-level APIs directly, instead of through the sagemaker library. The AWS Deep Learning Containers and SageMaker Scikit-Learn Containers source code: For a deeper understanding of the framework container environments.

Bring Your Own Container TensorFlow Algorithm - SageMaker Training Toolkit: This example provides a detailed walkthrough on how to package a TensorFlow algorithm for training using the SageMaker Training Toolkit. We have included also a Python file for local training that can run on your local M1 MacBook computer, for faster development. Note: Those examples were tested on macOS, Linux and Windows. Create an AWS account if you do not already have one and login. ... Windows users: Install Docker Desktop for Windows, WSL 2 and then, a Linux distro from the Microsoft Store.

For an end-to-end example that demonstrates how to integrate an Amazon A2I human review loop into a machine learning workflow, you can use a Jupyter Notebook from this GitHub Repository ... If you do not have an active SageMaker notebook instance, create one by following the instructions in Create an Amazon SageMaker Notebook Instance for the tutorial.

This repository provides the source code for use cases where applications interact with Large Language Model (LLM) using Langchain framework and text to SQL. There are two modes demonstrated here: Mode 1 - Use of LLMs with Langchain via Amazon ...

aws chatbot bedrock aurora semantic-search opensearch cdk claude sagemaker huggingface kendra llm langchain opensearch-serverless pgvector vectordb genai amazon-bedrock idefics ... Application implementation with business use cases for safely utilizing generative AI in business operations

These repositories contain essential components for initiating, managing, and customizing Large Language Model (LLM) training processes. You use the scripts from the repositories to set up and run the training jobs for your LLMs. ... main.py: This file serves as the primary entry point for initiating the process of submitting a training job to either a cluster or a SageMaker training job.

This workshop is aimed to help Feature Engineering and Machine Learning teams build Amazon SageMaker Feature Store capabilities for an enterprise scale solution. These notebook examples will get you started with using the SageMaker Feature Store.

See SageMaker V2 Examples for V2 documentation and examples. ... Unified Training & Inference: Single classes (ModelTrainer, ModelBuilder) replace multiple framework-specific classes · Object-Oriented API: Structured interface with auto-generated configs aligned with AWS APIs

You'll apply Amazon SageMaker built-in algorithms to a structured data example and a distributed training example showing different ways to set up nodes in a training cluster. Permissions: In order to complete this workshop you'll need an AWS ...

April 10, 2023 - Amazon SageMaker enables users to test and prototype locally. The Apache MXNet and TensorFlow Docker containers used in the AWS SageMaker are available on GitHub.

The example notebooks contain code that shows how to apply machine learning solutions by using SageMaker AI. Notebook instances use the nbexamples Jupyter extension, which enables you to view a read-only version of an example notebook or create a copy of it that you can modify and run. For more information about the nbexamples extension, see https://github.com/danielballan/nbexamples

This example shows a complete workflow for TensorFlow 2 with automation by the AWS Step Functions Data Science SDK, an older alternative to Amazon SageMaker Pipelines. To begin, SageMaker Processing is used to transform the dataset. Next, Local Mode training and Local Mode endpoints are demonstrated for prototyping training and inference code, respectively.