ms500in Goto Github PK

aiml-genai-multimodal-agent

amazon-codewhisperer-workshop

amazon-kendra-langchain-extensions

Samples to build Generative AI applications with LangChain and Amazon Kendra

amazon-lookout-for-vision-demo

A demo to test models trained by Amazon Lookout for Vision

amazon-lookout-for-vision-serverless-app

This project contains source code and supporting files for a serverless application which can be used for detecting defects in products in manufacturing assembly lines, storing inference results and sending real-time email notifications on defect detection.

amazon-lookoutmetrics-developer-guide

Amazon Lookout for Metrics Developer Guide: markdown build, sample applications, and other resources.

amazon-rekognition-custom-labels-a2i-automated-continuous-model-improvement

With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.

amazon-timestream-tools

avalanche

Prometheus/OpenMetrics endpoint series generator for load testing.

aws-fargate-ecs-masterclass

AWS Fargate & Elastic Container Service Masterclass - Course

aws-serverless-ecommerce-platform

Serverless Ecommerce Platform is a sample implementation of a serverless backend for an e-commerce website. This sample is not meant to be used as an e-commerce platform as-is, but as an inspiration on how to build event-driven serverless microservices on AWS.

aws-well-architected-labs

Hands on labs and code to help you learn, measure, and build using architectural best practices.

azure-functions-python-list-resource-groups

A sample to list resource groups in a subscription using Azure Functions Python

azure-quickstart-templates

Azure Quickstart Templates

azure-samples-python-management

This repo contains sample code for management libraries of Azure SDK for Python

bookdata-api

boot-opentelemetry-tempo

Project demonstrating Complete Observability Stack utilizing Prometheus, Loki (For distributed logging), Tempo (For Distributed tracing, this basically uses Jaeger Internally), Grafana for Java/spring based applications (With OpenTelemetry auto / manual Instrumentation) involving multiple microservices with DB interactions

caching

Source code from my microservices caching session at NFJS

chalice

Python Serverless Microframework for AWS

charts

Helm Charts

chinook-database

Sample database for SQL Server, Oracle, MySQL, PostgreSQL, SQLite, DB2

cicd-pipeline-train-schedule-jenkins

Train Schedule sample app for Jenkins exercises

cicd-pipeline-train-schedule-pipelines

Train Schedule sample app for Jenkins Pipelines exercises

cloud-lakehouse-labs

content-gitops

445, John Marx, 11/18/19

diffy

docker-fundamentals

Docker Fundamentals

examples

Kubernetes application example tutorials

filestat_exporter

Prometheus exporter gathering metrics about file size, modification time and other stats

flask-ecomm

:dollar: An eCommerce App built with Flask, Jinja, SQLite, jQuery and Bootstrap