Vipul Verma

• Created the Infrastructure to deploy Microservices on ECS through Terraform and Jenkins Patch the cloud servers to reduce the criticality of SecOps security concerns.
• Managed the scalability of instances through Autoscaling group policy(using lambda and CloudWatch event rule) and ECS service policy.
• Hosted 53 static website using CloudFront integrated with route 53 for node is application.
• Automated the deployment on Windows server with the help of SSM document and SSM parameters via Jenkins Deployment of services through Lambda.
• Deployed ML Models using AWS Resources (route53.ALB.Autoscaling Groups ECS,ECR) and automated the whole process through Terraform.
• Collaborated with cross-functional teams to identify and address performance bottlenecks, improving system reliability and scalability.

Client Project

• Creation of End to End Pipeline for the Automation of ETL job curated through Glue, Configured the SFTP for the file landing in s3 to kick off the ETL job on timeseries data and configured the powerbi with athena to visualize the data.
• Maintained and optimized AWS infrastructure to ensure high availability and scalability.

• Deployed ML Models using AWS Resources (route 53,ALB.Autoscaling Groups ECS ECR) and automated the whole process through Terraform.
• Created the Python Script that can download the specific objects from s3.
• Automated the copy of files from one s3 bucket to another in different region through jenkins.
• AWS ECR Image Scanning for vulnerability using Cloudwatch Eventbridge.
• Mounted s3 Bucket on EC2 Linux Instance using IAM role and s3fs.
• Scheduled the start and stop of an ec2 Instance through CloudWatch Event Rule that Invoke the Lambda function to do so and created the event pattern with SNS to trigger the email notification when the ec2 changes its state.
• Created the Email Notification for the Event change pattern for task running in the ECS.
• Deployment of ML model using App Engine and GKE.

ML Model creation using Google Ner

Extracted the text from the ixbrl tagged html documents along with the certain incorporated entities from that. Then, created a training data for model creation in the form of json file and import the json file on the Google Natural Language Entity Extraction and created the model. After that, Extracted text can be passed as an input to the model and from the response we can get the values.