fix: 🐛 fix grammar

more.html

@@ -171,25 +171,25 @@ <h3>Software Engineer(MLOps) - dataVediK</h3>
             Company's commercialized product [<a href="https://www.agoraiot.com/marketplace/drillvedik">DrillVedik</a>]
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-            <b class="italic-text">* Interactive Drilling Dashboard: </b>This is an <b>enterprise</b> product that I worked with two more engineers. Developed a <b>Plotly Dash</b> dashboard that visualizes processed data using Bootstrap, CSS media query, <b>Redis</b> and sqlalchemy. Also, implemented a <b>socket</b> service will notify when <b>Airflow</b> pipeline finished processing in order to <b>synchronize</b>(refresh) the dashboard's data.
+            <b class="italic-text">* Interactive Drilling Dashboard: </b>Developed an enterprise <b>Plotly Dash</b> dashboard in collaboration with two more engineers that visualizes processed data using Bootstrap, CSS media query, <b>Redis</b> and sqlalchemy. Also, implemented a <b>socket</b> service that will notify when <b>Airflow</b> pipeline is finished processing in order to <b>synchronize</b>(refresh) the dashboard's data.
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               <b class="italic-text">* CI/CD Pipeline: </b>Set up several <b>Azure Pipelines</b> for continuous development, testing and continuous deployment in <b>dev, test and prod</b> stages. Additionally, made a <b>Jenkins</b> pipeline to work with on-premise infrastructures.
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-              <b class="italic-text">* ML Pipeline: </b>Set up a <b>MLflow</b> server for machine learning experiment logging, parameter tuning, continuous training, model management and model serving.
+              <b class="italic-text">* ML Pipeline: </b>Hosted a <b>MLflow</b> server for machine learning experiment logging, parameter tuning, continuous training, model management and model serving.
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-              <b class="italic-text">* ETL Pipeline: </b>Working with a data engineer, set up an <b>Airflow</b> server for our data ETL pipeline.
+              <b class="italic-text">* ETL Pipeline: </b>Deployed an <b>Airflow</b> server in collaboration with another data engineer, for our data ETL pipeline.
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-              <b class="italic-text">* Prediction Task Manager: </b>Working with a front-end developer, designed and developed a <b>production</b> web application that supports job queuing and parallel processing for drilling speed prediction using JavaScript, <b>flask</b>, sqlalchemy, <b>celery</b>, RabbitMQ, gunicorn, Nginx, supervisord, Docker and AWS EC2, AWS Cognito Authentication, HTTPS
+              <b class="italic-text">* Prediction Task Manager: </b>Designed and developed a <b>production</b> web application in collaboration with a front-end developer that supports job queuing and parallel processing for drilling speed prediction using JavaScript, <b>flask</b>, sqlalchemy, <b>celery</b>, RabbitMQ, gunicorn, Nginx, supervisord, Docker and AWS EC2, AWS Cognito Authentication, HTTPS
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-              <b class="italic-text">* Drilling Status Detection: </b>Working with a domain expert, developed two <b>classification</b> models for detecting drilling status using Logistic Regression and Random Forest with the convenience of the MLflow server
+              <b class="italic-text">* Drilling Status Detection: </b>Developed two <b>classification</b> models in collaboration with a domain expert for detecting drilling status using Logistic Regression and Random Forest with the convenience of the MLflow server
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-              <b class="italic-text">* Drilling Speed Prediction: </b>Working with a domain expert, applied Gaussian Process <b>Regression</b> for feature synthesis based on geographical information as well as <b>feature engineering</b> based on correlation matrix and F1 score ranking, built a non-linear regression model using LSTM RNN.
+              <b class="italic-text">* Drilling Speed Prediction: </b>In collaboration with a domain expert, applied Gaussian Process <b>Regression</b> for feature synthesis based on geographical information as well as <b>feature engineering</b> based on correlation matrix and F1 score ranking, and built a non-linear regression model using LSTM RNN.
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raw-resume.html

@@ -46,7 +46,7 @@ <h5>Experience</h5>
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-                <b class="italic-text">* AWS:</b> Administrated the company's AWS account and several other customers' AWS accounts. Configured <b>VPC</b>, <b>Subnet</b>, <b>Security Groups</b>, <b>IAM</b> role and policies, <b>S3</b> lifecycle management, <b>EFS</b> access point, <b>EC2</b> instances, <b>RDS</b> instances, <b>Lambda</b> triggered by <b>SQS</b> or <b>EventBridge</b>, <b>SNS</b> and <b>SES</b>, <b>CloudWatch</b> metrics and alarms, <b>Route 53</b> DNS records, <b>Secrets Manager</b> for deployment secrets.
+                <b class="italic-text">* AWS:</b> Administrated DataJoint's AWS account and several other customers' AWS accounts. Configured <b>VPC</b>, <b>Subnet</b>, <b>Security Groups</b>, <b>IAM</b> role and policies, <b>S3</b> lifecycle management, <b>EFS</b> access point, <b>EC2</b> instances, <b>RDS</b> instances, <b>Lambda</b> triggered by <b>SQS</b> or <b>EventBridge</b>, <b>SNS</b> and <b>SES</b>, <b>CloudWatch</b> metrics and alarms, <b>Route 53</b> DNS records, <b>Secrets Manager</b> for deployment secrets.
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                 <b class="italic-text">* CI/CD: </b> Developed generic <b>Github Actions</b> reusable workflows used by <b>30+</b> repositories followed by <a href="https://www.conventionalcommits.org/en/v1.0.0/">Conventional Commits</a>, <a href="https://learn.microsoft.com/en-us/devops/develop/how-microsoft-develops-devops">Release Flow</a> and <a href="https://opengitops.dev/">GitOps</a> best practices, to automate build, test, release, publish private or open-source <b>Python</b> packages[<a href="https://pypi.org/search/?q=datajoint">PyPI</a>] or deploy <b>Docker</b> images[<a href="https://hub.docker.com/u/datajoint">Dockerhub</a>].
@@ -55,7 +55,7 @@ <h5>Experience</h5>
                 <b class="italic-text">* Kubernetes: </b> Provisioned Kubernetes clusters for development, staging and production environments using <b>k3d or kOps</b>. Developed utility <b>bash</b> scripts with <b>helm</b> and <b>kubectl</b> to manage Kubernetes clusters more efficiently, including configuring <b>Nginx ingress</b> controller, cert manager with <b>Let's encrypt</b> issuer, <b>Cillium</b> Container Network Interface(CNI), IAM Roles for Service Account(<b>IRSA</b>), <b>Cluster Autoscaler</b>, AWS Elastic Load Balancer(<b>ELB</b>) or deploying applications like Percona XtraDB Clusters, Keycloak, JupyterHub, Flask and ReactJS based web application, etc.
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-                <b class="italic-text">* Ephemeral Worker Clusters: </b> Designed and developed a worker lifecycle manager using Python in about a month to fulfill an <b>urgent</b> business requirement. This development <b>polls</b> jobs from a MySQL database, then provisions and configures ephemeral EC2 instances by <b>Packer(pre-build AMI), Terraform and cloud-init</b> to compute jobs <b>at scale</b>; implemented AWS S3 mount to significantly reduce raw data downloading <b>overhead</b> and added EFS as a file cache for intermediate steps to improve computation <b>failover</b>; configured <b>NVIDIA CUDA toolkit</b> and <b>NVIDIA container runtime</b> for <b>GPU</b> workers.
+                <b class="italic-text">* Ephemeral Worker Clusters: </b> Designed and developed a worker lifecycle manager using Python within one month to fulfill an <b>urgent</b> business requirement. This development <b>polls</b> jobs from a MySQL database, then provisions and configures ephemeral EC2 instances by <b>Packer(pre-build AMI), Terraform and cloud-init</b> to compute jobs <b>at scale</b>; implemented AWS S3 mount to significantly reduce raw data downloading <b>overhead</b> and added EFS as a file cache for intermediate steps to improve computation <b>failover</b>; configured <b>NVIDIA CUDA toolkit</b> and <b>NVIDIA container runtime</b> for <b>GPU</b> workers.
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                 <b class="italic-text">* Platform Automation: </b> To provision or terminate AWS resources using <b>boto3</b> or <b>Terraform</b>; manage customers' <b>RBAC</b> permissions using Keycloak and Github REST API; generating usage and billing report with <b>AWS S3 Inventory</b> report, <b>AWS CloudTrail</b> and <b>AWS Cost and Usage</b> report, made a <b>Plotly Dash</b> to analyze cost and usage efficiency.
@@ -64,13 +64,13 @@ <h5>Experience</h5>
                 <b class="italic-text">* Jupyterhub: </b> Configured and maintained Jupyterhub deployment on a Kubernetes cluster with <b>Node Affinity</b> to assign pods onto different nodes by requirements and <b>Cluster Autoscaler</b> along with <b>AWS Auto Scaling Group</b> to accommodate <b>100+</b> active users; improved base images' <b>build time</b> and maintenance <b>overhead</b>.  
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-                <b class="italic-text">* Observability:</b> Implemented small part of the metrics and alerts using <b>AWS CloudWatch</b>, and then later integrated <b>Datadog</b> for Kubernetes clusters' and ephemeral EC2 instances' metrics and logging through <b>OpenTelemetry</b> protocol, synthetic API testing, UI/UX monitoring.
+                <b class="italic-text">* Observability:</b> Implemented a small part of the metrics and alerts using <b>AWS CloudWatch</b>, and then later integrated <b>Datadog</b> for Kubernetes clusters' and ephemeral EC2 instances' metrics and logging through <b>OpenTelemetry</b> protocol, synthetic API testing, and UI/UX monitoring.
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-                <b class="italic-text">* Security:</b> Set up codebase <b>vulnerability</b> scan with FOSSA; Set up <b>AWS Secrets Manager</b> working with <b>External Secret Store Operator</b> to secure Kubernetes secrets; Deployed and administrated self-hosted <b>Keycloak</b> for <b>RABC</b> authentication, further integrate it with <b>AWS IAM</b> as an <b>identity provider</b> to access AWS resources through <b>STS</b>, learned about OpenID Connect(<b>OIDC</b>) authentication flow such as authorization code flow, client credential flow, password grant flow etc.
+                <b class="italic-text">* Security:</b> Set up codebase <b>vulnerability</b> scan with FOSSA; Set up <b>AWS Secrets Manager</b> working with <b>External Secret Store Operator</b> to secure Kubernetes secrets; Deployed and administrated self-hosted <b>Keycloak</b> for <b>RABC</b> authentication, further integrated it with <b>AWS IAM</b> as an <b>identity provider</b> to access AWS resources through <b>STS</b>, enabled OpenID Connect(<b>OIDC</b>) authentication flows such as authorization code flow, client credential flow, password grant flow etc.
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-                <b class="italic-text">* MySQL Database:</b> Maintained a self-hosted <b>Percona XtraDB Clusters</b> on database <b>daily backup</b> stored on <b>S3</b>, <b>mysqldump</b> backup redundancy, Point-in-Time Recovery(<b>PITR</b>), <b>deadlock</b> detection, slow query log. 
+                <b class="italic-text">* MySQL Database:</b> Maintained a self-hosted <b>Percona XtraDB Clusters</b> on database <b>daily backup</b> stored on <b>S3</b>, <b>mysqldump</b> backup redundancy, Point-in-Time Recovery(<b>PITR</b>), <b>deadlock</b> detection, and slow query log. 
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