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AWS Certified DevOps Engineer – Professional

A highly motivated and skilled Cloud Developer with 2 years of experience in designing cloud architecture using AWS and Azure to build and deploy scalable and secure systems

An experienced Backend Developer in designing restful API and database schema and deploying cloud backend services 

Also proficient in Data Science such as machine learning and deep learning algorithms and their applications to real-world problems

台灣大學

Cloud Developer | Backend Developer| Data Science

Syunya Jhao

eCloudvalley 伊雲谷數位股份有限公司

Backend/Data Engineer
eCloudvalley Digital Technology Co., Ltd  •  Feb 2021 - Present


1. Project: Real Time Dashboard Project using OCR- POC

"This project is to demonstrate the entire process from image capture to dashboard. The process includes labeling , cropping , recognizing the cropped equipment-screen-captured images, storing the recognized information, and displaying the information on a real-time dashboard."

- Redesigning the VGG Image Annotator for labeling equipment-screen-captured images and storing the boundary data of labels in a SQL Server database 

- Using OpenCV to crop the incoming image based on the boundary data and recognizing the cropped images by PaddleOCR

- Storing the recognized information and corresponding labels in SQL Server Database 
Using Power BI Desktop to display the information on a real time dashboard 

Result: quickly demonstrate how to build the end to end process from image capture to real-time dashboard 

2. Project: Real Time Plastic Injection Data Dashboard Project

"This project is  to send plastic injection data to IOT Hub, combined with mold data, to display the data on an interactive dashboard that provides up-to-date information . "

- Using Azure IOT Hub and Azure Event Grid to trigger Azure Functions to process and store data  into MYSQL Database

- Using Grafana to design an interactive dashboard with real-time updates 

- Hosting a static website on Azure Blob Storage and and using Azure Functions  and FastAPI as backend to manage mode data

- Using Azure Logic Apps to send an email alert when there is abnormal data detected 

Result: Monitoring factory machine data remotely and in real-time to improve productivity

3. Project: Equipment Damage classification and prediction Project

"The goal of this project is to perform data analysis using collected equipment damage features and label data, train a classification model to predict damage severity labels, and forecast the trend of future damage severity.  "

- Using Pandas, NumPy and Pandas-Profiling to conduct exploratory data analysis (EDA)
Using  Seaborn heatmap to visualize the correlations and boxplot to visualize the distribution  of damage severity  based on month, weekday and hour  
- Using the XGBoost algorithm to classify damage severity

- Using recurrent neural network (RNN) to forecast the trend of future damage severity over the next six months in a time series analysis

Result: The accuracy of the classification model is 99% evaluated using the test dataset, and the important features that contribute the most to the classification task are identified. The time series forecast will provide recommendations on which equipment should be maintained over the next 6 months. 
 
4. Project: Customer Data Platform Cloud Architecture Solution Project

"This project is  to provide design of a scalable cloud structure for the open source Customer Data Platform (Apache Unomi ) which can handle large volumes of traffic, ensure high availability and scalability of the service " 

- Configuring the Amazon API Gateway  to handle the API requests distributed to AWS Simple Queue Service (SQS) and AWSElastic Load Balancer (ELB) to provide different levels of traffic control

- Setting up AWS ELB to deploy the Apache Unomi servers in an autoscaling group, allowing the infrastructure to automatically add or remove servers based on traffic levels or other conditions 

- Setting up AWS SQS which acts as a buffer to handle bursts of traffic, so that incoming requests are stored in the queue and sent to the Apache Unomi servers in a controlled manner 

- Using AWS OpenSearch for data storage and data visualization

Result: providing system design  report  to achieve the goal of reducing the cost of AWS OpenSearch

5. Project: Data Streaming and Visualization Project

"This project is to  build a platform deployed in  Azure and AWS to store streaming data and further visualization."

- Azure Platform
- Using Azure  Event Hubs/Azure IOT Hub to stream data into  Azure Blob Storage

- Using Azure Event Grid to trigger Azure Function to  analyze  and store the data into Azure SQL Server

- Using Power BI for visualization

- AWS Platform
- Using AWS Kinesis to stream data into AWS S3

- Using AWS Lambda to analyze the data 

- Using AWS OpenSearch for visualization 

Result: the platform that can fulfill the need to store 2 million units of sensor data by streaming it into storage within a time frame of 4 seconds 

台灣台北市

好想工作室培訓學員，以後端為主，簡單前端為輔(HTML/CSS/JS)，主要語言為PHP。
1.	使用作業系統與資料庫：
        (1)  作業系統：透過了解作業系統運作原理，優化程式運作。
        (2) 使用Linux(Ubuntu)作業系統。
2.     資料庫系統：
        (1)  使用SQL（MySQL）以及索引加快查詢速度。
        (2) 使用NoSQL(MongoDB):通過MongoDB University課程M001。
3.	開發RESTful API：
        利用RESTful API的形式開發API。
4. 	PHP語言開發專案：
	(1) PHP的物件導向編程的使用結合SOLID的軟體設計概念開發純PHP專案。
	(2) 成果：使用原生PHP寫出一個簡單MVC框架。
Github連結：https://github.com/yayajojo/php-mvc-framework
5.	使用Laravel框架開發專案
使用CSS/HTML/JS/Laravel開發。
        (1)  作品集成果一：類twitter的社交網站。
        (2) 作品集成果二 ：家庭記事板網站，以TDD方式開發，使用者可以撰寫與其他人分享計畫與分配任務。
6. 雲端部署：AWS/GCP






好想工作室

後端工程師學員

GAEA是動物疫苗研發公司，任職期間負責國外客戶與研發團隊的溝通橋樑。1. 了解需求：透過客戶或研究報告了解當地動物疫苗需求，如各廠商市占率、希望的疫苗使用方式、成本要求等。
2. 促進溝通：讓團隊了解客戶對成本與技術需求，並在雙方間溝通實際上可能的限制與突破口，並定期分享與交流資訊。
3. 促進交流：籌辦印尼動物疫苗研討會，增進產品曝光度。
 


GAEA, Biotech

國外業務

工作內容為協助客戶申請美國專利。
1. 閱讀技術文件:申請前須閱讀申請技術的相關資料，撰寫英文申請文件或翻譯中文相關文件送審，等待美國專利審查員會就送審資料給予核准或不核准專利意見。
2. 客戶溝通:就不核准專利意見，我們會(1)先分析溝通意見書的邏輯與所引用的文獻是否合理後產生初步的建議；(2) 與客戶溝通後選擇符合客戶需求的回應策略。


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