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Name: Michael H Bitar
Type: User
Bio: Certified: GCP Data Engineer Pro, TensorFlow Developer, AWS AI/ML Specialty, AWS Data Analytics Specialty AWS DevOps Pro, AWS Sol. Arch Pro
Location: Springfield, VA
Michael H Bitar's Projects
aws-datalake-poc-video
AWS hosted enterprise Data Lake with both batch and realtime data pipelines.
aws-dms-postgres-onprem-to-rds-v1
This guide is a walk-thru of how to establish a migration pipeline from an on-prem Postgres 12 database to an AWS RDS Postgres 12 database using the AWS Database Migration Service.
aws-ec2-asg-alb-video
Live walk-thru of AWS EC2 autoscaling and load balancing
aws-iot-esp32-arduino
This solution demonstrates how to integrate Espressif's ESP32 microcontroller with the AWS IoT ecosystem using MQTT as the pub/sub protocol to exchange messages/commands between the ESP32 and the IoT service.
aws-iot-raspi-sensors
Using AWS IoT, ingest environmental and motion sensors data from a Raspberry Pi SenseHAT, then using AWS IoT Rules, store in DynamoDB, execute Lambda to test sensor values, and send notifications via SNS.
aws-org-governance-security-guardrails
Organization service control policies (SCP) that I apply frequently to OUs to enforce best practices and protect baseline security and governance resources in sub-accounts
aws-s3-security-guardrails
Controls to secure S3 data by enforcing rules such as IP sources, VPC origins, encryption, MFA, etc.
aws-serverless-devops-video
AWS based serverless DevOps pipeline using CodePipeline, CodeDeploy, CodeBuild, and CodeCommit with chat integration.
aws-utility-scripts
AWS CLI/Bash automations that I have collected over time to help me configure AWS resources.
breast_cancer_ml
ML pipeline using the Breast Cancer dataset with xgboost and other popular models
mbx-getstock-aws-puppeteer
A serverless solution to gather stock info from the web using Puppeteer on AWS
ml-bank-telemarketing
Using TensorFlow to assess bank telemarketing campaign impact
mlflow-local
Deploy MLflow locally to track model development
titanic_ml_multimodel
Deploy a pipeline to train multiple ML model and compare results.
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