Distributed Deep Learning on AWS Using CloudFormation (CFN), MXNet and TensorFlow

An easy interface to query the EC2 metadata API, with caching.

🔗 A URL shortening service built using Flask and MySQL

Some useful AWS scripts

Ansible playbook to deploy distributed technologies

Python examples on AWS (Amazon Web Services) using AWS SDK for Python (Boto3). How to manage EC2 instances, Lambda Functions, S3 buckets, etc.

Example project showing how to use Pulumi locally & with TravisCI to create Infrastructure on AWS - and then how to use & integrate Pulumi with Ansible to install Docker on the EC2 instance & continuously test it with Testinfra & pytest (TDD)

An AWS EC2 Autoscaler for mutable architectures.

A CLI tool that creates AWS spot instances on the fly

Learn AWS Security by Example

Shortcuts for AWS EC2 Instance Control from the command-line: list, start, stop and ssh

A end-to-end real-time stock market data pipeline with Python, AWS EC2, Apache Kafka, and Cassandra Data is processed on AWS EC2 with Apache Kafka and stored in a local Cassandra database.

Shorthand commands to work faster on AWS locally

Start or Stop an AWS EC2 Instance with Python

AWS services backup using Step functions

Opinionated sample on how to build and deploy a RAG application with Amazon Bedrock and OpenSearch

Ansible playbook for provisioning data science EC2 spot instance

This project creates Lambda function that automatically add required AWS Identity and Access Management (IAM) policies to current Amazon Elastic Compute Cloud (Amazon EC2) instance profiles or associate a profile to EC2 instances without a profile associated.

Built a Data Pipeline for a Retail store using AWS services that collects data from its transactional database (OLTP) in Snowflake and transforms the raw data (ETL process) using Apache spark to meet business requirements and also enables Data Analyst create Data Visualization using Superset. Airflow is used to orchestrate the pipeline

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD