Hi there!
Welcome to my home where technology meets innovation. Explore the latest in AI, data, and tech with me, Let's explore together!
Brief Background
Hi, I'm Hyacinth Kwadwo Ampadu, an AI professional from Ghana. With over 4 years of experience working remotely for companies worldwide, I'm passionate about innovation and creativity in the exciting world of AI. Throughout my career, I've worked in various domains, from healthcare to finance, delivering successfully to clients in every project I undertake. From predictive modeling to time series analysis to Natural language processing and now Generative AI, I take pride in pushing the boundaries of what's possible. Join me on my journey as I continue to innovate and explore the endless possibilities of AI.
SKILLS & SERVICES
Python Development
Cloud Computing
Data Science
Machine Learning
Engineering & Operations
Natural Language
Processing
Generative AI
Work Experience
Some projects completed for clients include:
May 2023 - Date
-
Spearheaded the backend and AI development of a robust enterprise chatbot application leveraging state-of-the-art large language models such as GPT-3.5 and GPT-4, using Python for the development.
​
-
Utilized Langchain as the language model framework, incorporating Pinecone as the vector database for efficient data storage and fast retrieval, and implemented optimized meta filters to ensure versatile company usage while maintaining data privacy.
​
-
Engineered a comprehensive scraper capable of extracting information from various sources, including entire websites, GitHub, sitemaps, PDFs, Word documents, and more, using Flask for the web API framework and Pusher for real-time event streaming.
​
-
Orchestrated deployment on Google Cloud Platform (GCP) leveraging services like Cloud Run, cloud tasks, monitoring tools, CI/CD pipelines with GitHub Actions and GCP Cloud Build, and containerization with Docker, ensuring adherence to MLOPS and best software engineering practices.
​
-
Collaborated effectively with cross-functional teams, particularly front-end developers, for seamless integration and user-friendly implementation.
August 2022 - February 2023
-
Worked as a sole ML engineer on a team to develop and deploy ROBERTA and GPT3 models for the Ghanaian political domain.
-
Improved tweet engagement by 13% with domain-adapted models.
-
Adapted models for a better understanding of the Ghanaian political context.
-
Achieved 75% improvement in the model's ability to understand the context, reducing perplexity/confusion.
-
Worked as a sole ML engineer on a team to develop and deploy ROBERTA and GPT3 models for the Ghanaian political domain.
-
Improved tweet engagement by 13% with domain-adapted models.
-
Adapted models for a better understanding of the Ghanaian political context.
-
Achieved 75% improvement in the model's ability to understand the context, reducing perplexity/confusion.
July 2020 – May 2022
-
Worked as an ML engineer with an Australian startup to develop models for predicting future NFT prices, achieving 10% ROI on NFTs.
-
Developed a data pipeline using Spark to collect and preprocess data from multiple sources including blockchain, parsec API, open sea API, and social media.
-
Implemented NLP techniques and conducted feature engineering and selection on text data.
-
Deployed results on a Tableau dashboard for stakeholders.
January 2021 – January 2022
-
Collaborated with a startup in the US to develop a predictive model that estimated rail temperatures in Jacksonville for improved railroad safety and infrastructure protection.
-
Built an accurate and comprehensive model by applying the ML model to historical data with over 25 engineered features including temperature, elevation, humidity, and time of day.
-
Developed data pipelines and processing workflows using airflow and docker for seamless data ingestion and cleaning.
-
Deployed the model as an API, allowing easy integration into the startup's platform and continued enhancement to its performance and capabilities.
May 2020 – June 2021
-
Built a highly accurate and interpretable deep-learning model to predict the COVID-19 mortality rate and ICU admission likelihood for time-bound and resource-limited scenarios for a client in Abu Dhabi.
-
Conducted extensive data pre-processing and cleaning, fine-tuned the model using state-of-the-art techniques, and identified the most influential features contributing to its predictions, resulting in publication in a high-impact medical journal.
June 2019 – February 2020
-
Worked as a data scientist for consulting firm in India by developing robust machine learning models to predict shipping rates, resulting in a 15% cost reduction for customers.
-
Collaborated with cross-functional teams to preprocess, analyze, and visualize large datasets containing information on past orders and shipping rates, and deployed the ML model using MLOps best practices.
Personal Projects
Project Description:
With the rise of social media use among younger generations, it is important to ensure that the content they are exposed to is appropriate and free from harmful language.
This project builds a web application designed to promote safe online social interactions for children. The app flags these tweets, alerting the user of the potentially harmful content, and then offers recommendations on how to rephrase their tweets that are more kid-friendly or appropriate for children.
​
Code:
https://github.com/JoAmps/KidFriendlySocial
​
Blog:
​
​
Project Description:
Lead conversion is a crucial aspect of business, especially for app and website companies. Identifying and predicting which leads are likely to become paying customers is important since those individuals contribute a lot to the company's profit margin. This project deals with predicting these leads so that the required and appropriate arrangements are made to enable them to complete the process of converting, and to keep making profits for the company and business.
​
Code:
https://github.com/JoAmps/Lead_conversion_prediction_for_a_mobile_app_company
Blog:
​
Project Description:
Predicting and identifying the sentiment of users on an app is key for businesses and app creators to identify what the customers really feel and think about the app and make the necessary changes to address their needs to keep them from churning and using other fitness apps, so as to keep making a profit. This project deals with building models to predict sentiments of reviews from users and deploying it on the cloud, on the various fitness apps so businesses can make informed decisions going forward
​
Code:
https://github.com/JoAmps/bert-based-health-and-Fitness-sentiment-system
Customer churn prediction in a Vehicle Insurance company In Ghana
Project Description:
Customer churn prediction is an important business activity for any company. The ability to predict this is very key so that companies identify such individuals and make the necessary arrangements to keep them since the company thrives on getting customers subscribed to their insurance. This project deals with solving this important problem in this company, to help the company keep making a profit.
​
Code:
https://github.com/JoAmps/Churn-prediction-in-a-vehicle-insurance-company-in-Ghana
Project Description:
The ability to forecast the prices of any commodity is crucial for companies, traders, and individuals, to know the price of their commodity in the future, so they make better plans using this knowledge. NFTs are the new craze that everyone is jumping on, there's potential money in it, and this project deals with forecasting these prices, to know if the NFT price would grow or decline in the future, so individuals can know whether to buy this nft in the particular week or sell it if they already own one
​​
Code:
https://github.com/JoAmps/Forecasting-average-weekly-prices-of-the-Parallel-Alpha-nft
Blogposts
Here are some of my Blog posts, click on the respective images to read!
Unlocking ML Opportunities: Building a Strong Portfolio for Self-Taught Career Launch- PART 1
I wrote about the approach to building quality portfolio projects to launch your career in ML
Transforming Leads into Customers: Optimizing Lead Conversion with Predictive Modeling
I wrote about the approach to optimizing lead conversion utilizing predictive models
MLOps Best Practices for Machine Learning Applications: A Case Study of KidFriendlySocial
I wrote about the approach to building machine learning systems following the MLOPs best practices
My Experience in the Udacity Machine learning DevOps(MLOPS) nano degree
I wrote about my experience, and the courses and projects I built in this nano degree
Dropout in Deep learning
Understanding Dropouts in Deep Learning to reduce overfitting
Random Forests Understanding
Intuition and Implementation on a key algorithm to reduce overfitting in tree based algorithms
Decision Trees
I wrote about the intuition and implementation of the first tree-based algorithm in machine learning
Understanding of Support Vector Machine (SVM)
Explanation of the support vector machine algorithm, the types, and how it works
Normalization in Deep learning
I wrote about the different types of Normalization in Deep Learning. A very useful technique to avoid overfitting and generalize your model better.
Yolov3 and Yolov4 in Object Detection
Explanation of object detection with various use cases and algorithms. Specifically, how the yolov3 and yolov4 architectures are structured, and how they perform object detection
Linear and Logistic Regression
I wrote about the intuition and implementation behind the base algorithms for supervised machine learning
Research Publications
My research papers that have been published
Interpretable Tabular Deep Learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients
The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives, this is what our publication addresses
Click on image to be redirected to the paper
