10 Academy profile site
Henok Tilaye
Addis Ababa, Ethiopia
Addis Ababa Science and Technology University (2015-2021)
BSc. (Computer Engineering)
Email: henoktilaye7@gmail.com
Programming Languages
Python
JavaScript
C++
ML Tools
PyTorch, TensorFlow
Scikit-Learn, Pandas
Kafka, Airflow, Spark
SQL, MongoDB
Automation Tools
Docker
GitHub Actions
MLFlow, DVC, CML
Unit-Testing
General Skill-Sets
ETL Pipeline Building
Data Visualization
ML Model Life Cycle Management
Web-Scraping
About me
Henok Tilaye is a Junior Machine Learning Engineer with two years of professional experience. He has a bachelor's in Computer Engineering and also has experience in Systems Penetration Testing, Back-End Development, and Web-Scraping. He is also adept with Data Processing, Visualization, Modeling Pipelines, and Systems Deployment.
Education
(Machine Learning Engineering/Data Engineering/WEB 3 Engineering)
A Project based Intensive training where I got to work on ten projects that helped me develop the following skill-sets:
Machine Learning Pipeline Development
Data Engineering Principles and large Scale Implementations
WEB 3 dAPP Development
Statistical Reasoning
BSc. (Computer Engineering)
Relevant Courses
Introduction to AI
Embedded Systems
Digital Signal Processing
Probability and Statistics
Data Structures & Algorithm Design
Image Processing & Pattern Recognition
Implemented a web-app used for technical analysis of crypto tokens.
Web-Scraping and preparation of Large Scale datasets by integrating WEB3 APIs.
Improved old scrapers' speed by more than 10 times utilizing concurrent programming.
Decreasing downtime and increase portability by containerizing all system blocks.
Optimized old docker containers to save storage and compute.
Implemented an Event-Driven System for trade order streaming with Kafka (Faust).
Updated Legacy code-bases to add features and remove deprecated libraries.
Developed Deep Learning models to extract insight from image data.
Built an in-house visualization solution for Computer Vision Models.
Prepared datasets for Computer-Vision projects.
Built an Image segmentation model to help the agriculture sector.
Increased efficiency to the model building process by utilizing MLFlow.
Trained a computer vision model for detecting plant disease.
Trained a computer vision model for segmenting agricultural land features using satellite imagery.
This project focuses on using causal inference to answer causal questions related to breast cancer cases. It uses a tabular data with features about cell samples of different individuals. The data used in this project is taken from kaggle. It can also be downloaded from the UCI Machine Learning Repository.
This was a team effort to build a Speech to text engine that would be able to transcribe two African languages. These were Amharic and Swahili. We explored some deep-learning architectures that would give the best results. And at the end, we build a web-app that a user could use to interact with our model.
This was a project for helping an advertisement company measure the effectiveness of the ads they made for a client, It was also build in a way that the company could use the system for measuring future ads performance. The project was completed in teams. I was responsible for setting up the MLOps components and the ML based A/B testing pipeline.