Hello, It's Me

Shongo On'osala Aaron

And I'm

I'm An Operation Team Member At Nobel Learning PBC
A Machine Learning Engineer Trainee & Advanced Generative AI Developer Trainee At Google

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About Me

I'm Shongo On'osala Aaron, An Artificial Intelligence And Machine Learning Engineer, Data Scientist, Data Analyst, and polyglot. My journey began back in Kisangani, Democratic Republic of the Congo, and has since brought me to my current home in Kampala, Uganda. My formal education as a mathematician did not initially cover programming, AI, or machine learning. Instead, it focused on developing my analytical thinking and fostering a boundless enthusiasm for acquiring new wisdom with a profound passion for continuous learning and exploration. I first encountered the concept of AI in secondary school, and at the time, I thought it was something unreal, like something out of a science fiction movie. However, my curiosity was piqued, and I began researching AI extensively. I decided then that if I were to pursue any IT related field, it would be AI, as it offered a vast array of opportunities and challenges. In 2021, I took a significant step towards my dream by enrolling in a Higher Education Certificate in Information Technology. During the final semester, we had a Field Study in ICT Literacy. Mr. Kato Kenneth, our lecturer, proposed several research topics, one of which was Artificial Intelligence and Machine Learning. This topic immediately caught my interest, and I chose to focus my research on it. This decision brought back my secondary school memories and reinforced my commitment to pursuing a career in AI.

I sought advice from two of my friends, one was studying Computer Science and the other already specializing in AI. The Computer Science route was appealing, but my passion for AI was stronger.

More About Me

My Services

AI Model Development & Deployment

I specialize in designing, developing, and deploying advanced Generative AI models tailored to solve specific business challenges. This includes everything from initial model creation to integration and implementation in real-world environments, ensuring seamless operation and maximum efficiency.

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Machine Learning

Fom Designing to productionisation, optimisation, operation and maintainance of Machine Learning CI/CD pipeline, I ensure flawless integration and efficiency at every stage of the ML lifecycle into production envirnments. My approach prioritizes scalability, reliability, and maintainability, ensuring that ML models transition smoothly from experimentation to real-world deployment, driving business impact and innovation.

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Data Science

I help businesses extract meaningful insights from their data by Utilizing a combination of statistical and machine learning techniques. My services include building predictive models, conducting deep data analysis with advanced statistical techniques to extract parttens from large datasets, develop machine learning model to predict future trends, and providing actionable recommendations to drive informed decision making.

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Data Analysis

I offer comprehensive data analysis services to uncover patterns, trends, and insights within your datasets. By applying various analytical methods, I provide detailed reports and visualizations that inform strategic planning and business decisions.

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My Latest Pjoctes

What I Will Do For You

Harnessing the Power of Advanced Deep Learning

Harnessing the Power of Advanced Deep Learning Techniques for Early Detection and Classifying Maize Leaf Diseases in Uganda

I develop an advanced AI model for maize leaf disease classification, utilising advanced computer vision algorithms to distinguish between healthy maize leaves and those affected by Maize Streak Virus, Maize Lethal Necrosis, Maize Leaf Blight, and Fall Army worm. By expanding the dataset from 26,043 to 50,000 images through several data augmentation techniques, I optimised the models accuracy from 94% to 99.7% over 100 epochs. Despite computational challenges, I selected best, scalable and most reliable AI model for its speed and efficiency, creating a reliable, scalable solution for early disease detection. This project showcases my expertise in machine learning and computer vision, providing farmers with a fast, accessible tool for improving crop health and yield as well as increasing agricultural output.

Collaboration: Makerere AI Lab And Lacuna Fund

Artificial Intelligence Computer Vision Generative AI AI in Agriculture
My Google Training Experiences

My Google Training Experiences

These projects showcases my hands-on expertise in AI and machine learning through advanced Google Cloud training. In the Generative AI for Developers Learning Path, I gained practical experience in building and deploying generative models for application development, focusing on real world implementation. The Machine Learning Engineer Learning Path, provided in-depth training on the end to end ML lifecycle, from system design, smodel development to optimization and deployment. Through structured courses, labs, and skill badges, I developed the ability to build scalable, production-ready ML solutions using Google Cloud technologies. These programs have refined my skills in designing AI-driven applications and optimizing machine learning workflows for efficiency and scalability. With this expertise, I am equipped to develop innovative, high-impact AI solutions that address complex business challenges.

Generative AI Natural Language Processing Artificial Intelligence Google Clound Deep Learning Machine Learning Automation
Kinshasa Traffic Speed Prediction For Yango

Kinshasa Traffic Speed Prediction For Yango Group, International Tech & Transport Company

The Kinshasa Traffic Speed Prediction project was a collaboration with Yango and Zindi Africa, aimed at optimizing route planning and improving ETA predictions for Yango's ride hailing services in Kinshasa. Using traffic data collected in September 2023, my goal was to develop a machine learning model that could predict average traffic speeds along major roads every 15 minutes, taking into account time of day variations and traffic patterns. I began by cleaning the data, addressing missing values and inconsistencies, then extracted relevant features such as time of day, weather, and historical traffic patterns. Through exploratory data analysis, I visualized and identified significant trends, which helped in feature selection. I initially tested models like Linear Regression and Random Forest, but ultimately chose TensorFlow Decision Forest due to its ability to handle complex structured data. After training the model with k-fold cross-validation and hyperparameter tuning, it achieved an impressive RMSE of 1.54, which demonstrated its accuracy. The model significantly improved route optimization for Yango, increasing efficiency by 20%. This also led to more accurate ETA predictions, enhancing user experience by reducing waiting times and improving punctuality. Despite challenges such as data variability and fine-tuning, the solution successfully transformed Yango's route optimization process, resulting in improved operational efficiency and better resource allocation for both drivers and passengers.

Collaboration: Yango Group, International Tech & Transport Company And Zindi Africa

Data Science Machine Learning AI in Transport Road Traffic
AirQo Africa, Air Quality And Pollution Estimation

AirQo Africa, Air Quality And Pollution Estimation In Africa Cities

The AirQo Africa Air Quality and Pollution Estimation project, in collaboration with AirQo Africa, Mozilla Foundation, and Makerere University AI Lab, aimed to estimate PM2.5 levels across eight African cities. These estimates were derived from satellite observations based on Aerosol Optical Depth (AOD), offering a scalable, cost effective solution to air pollution monitoring, particularly in regions where ground sensors are expensive to deploy. I developed a machine learning model to predict PM2.5 levels, leveraging satellite-derived AOD data and ground-based observations. After evaluating several models, I chose the TensorFlow Decision Forest model for its ability to capture complex relationships between AOD and PM2.5. I applied the leave one out cross validation technique to train the model, ensuring robust validation despite the small dataset size. The TensorFlow Decision Forest model achieved an impressive RMSE of 17.69, demonstrating its accuracy in estimating PM2.5 levels. This model is now deployed through AirQo's digital platform, enabling communities to access crucial air quality data, which empowers local interventions to improve air quality and protect public health. This project highlights the potential of AI in addressing significant environmental health risks. By providing reliable, actionable data, I contributed to improving public health, fostering environmental justice, and supporting climate change mitigation efforts across Africa. Despite challenges with data quality, model complexity, and resource constraints, the project underscores the importance of innovative, data driven solutions for global health and environmental sustainability.

Collaboration: AirQo Africa with Makerere AI Lab, supported by Google and Mozilla Foundation.

Climate Change Machine Learning PM2.5 Pollution Data Science
Sales Data Analysis for Retail

Semantic Search Multi-PDF Summarizer

I developed the Semantic Search PDF Summarizer a state-of-the-art and cutting-edge AI-powered tool to efficiently extract key insights from lengthy PDF documents. By integrating advanced natural language processing (NLP) and generative AI techniques, I designed this system to process PDFs, summarize their content, and provide concise, contextually meaningful information. Unlike simple keyword extraction, my summarizer incorporates semantic understanding to generate more accurate and relevant summaries. I built the project to support PDF ingestion by parsing content, including text and metadata, using libraries like PyPDF2, PDFMiner, and PDFPlumber. It handles complex layouts, embedded images, and tables, ensuring comprehensive text extraction. Leveraging fine tuned generative AI models, including OpenAI Chat GPT, I enabled the system to understand document semantics and offer both extractive and abstractive summarization modes. Users can customize summary length and retrieve key information through natural language semantic search, making the tool highly interactive and efficient. The technical architecture involves multiple stages, including text extraction, preprocessing, model integration, and semantic search. I implemented vector databases to index documents, allowing similarity searches to retrieve the most relevant sections based on user queries. The system provides interactive outputs, such as downloadable summaries and web-based interfaces using Streamlit. This tool is particularly useful for researchers, academics, corporate and legal professionals, and content creators, as it simplifies complex documents into digestible formats. I addressed challenges like complex formatting, scanned PDFs requiring OCR, AI model limitations, and scalability concerns through optimization strategies. Looking ahead, I plan to enhance the system with multi-language support, contextual summaries, integration with collaboration tools, and mobile accessibility. By bridging the gap between vast amounts of information and actionable insights, I created the Semantic Search PDF Summarizer to enhance productivity and streamline information retrieval.

Generative AI Natural Language Processing Artificial Intelligence Deep Learning
Harnessing the Power of Advanced Deep Learning

Cocoa Object Detection

The Cocoa Object Detection project aimed to create a sophisticated AI model capable of identifying cocoa pod at various maturity stages of development. The dataset used in this project, contained cocoa pods in four categories: Mature_Unripe, Immature, Ripped, and Spoilt. Originally in PASCAL VOC XML format, the dataset consisted of over 6,000 images, divided into training, validation, and test sets. My main challenge was converting the data into a format suitable for YOLO, which involved writing a script to parse XML files and translate bounding box coordinates and class labels. I successfully converted over 6,000 XML annotations into the YOLO format, ensuring accurate data translation. Using YOLOv10, I trained the model and optimised it to detect cocoa pods in both images and videos. The result was a 99.8% accuracy on the validation dataset, reflecting both the models effectiveness and the successful data conversion. Despite facing limited resources and tight deadlines, I managed to optimise the training process and make the most of the available resources. This project demonstrates how AI can advance agriculture, offering a scalable solution for monitoring cocoa production in Uganda, while also showcasing my ability to solve complex data conversion challenges and achieve tangible results by productionising Artificial Intelligence model.

Collaboration: Makerere AI Lab And Lacuna Fund

Computer Vision AI in Agriculture Automation Generative AI
Sales Data Analysis for Retail

Analyzing Top Films: Genre, Profitability, and Worldwide Gross Using Tableau

The film industry constantly seeks to maximize profitability while producing critically acclaimed movies. However, achieving a balance between high ratings, profitability, and wide audience reach is challenging. This project addresses the need to understand the relationship between a films genre, its critical reception, profitability, and global earnings. Identified high-rating genres, contributing to more informed genre selection for filmmakers. Provided actionable insights on global earnings factors, aiding studios in optimizing release strategies, this project offers valuable insights into the film industry's dynamics, enabling more strategic decision-making for filmmakers and studios.

Data Analysis Business Analysis Predictive Modeling Statistical Analysis

Clickbait Detection

The Clickbait Detection project aimed to develop a Generative AI model that classifies headlines as either clickbait or non-clickbait. Clickbait headlines are designed to grab attention and encourage clicks through sensational language, and detecting these headlines is key to improving content quality and user experience on online platforms. I leveraged Natural Language Processing (NLP) techniques to address this challenge, focusing on a transformer-based model, specifically BERT (Bidirectional Encoder Representations from Transformers). BERT was ideal due to its ability to understand the context of words in sentences. The dataset consisted of labeled headlines (clickbait or non-clickbait) from various online sources. I started by preprocessing the text data, tokenizing the headlines using BERT's tokenizer. Afterward, I selected BERT for sequence classification and fine tuned the pre-trained model for my specific task. The model was trained for 50 epochs using the AdamW optimizer and a PyTorch DataLoader for batch processing. I evaluated the models performance using the F1 score, ensuring a balance between precision and recall. Despite challenges such as resource constraints, which were addressed by using Kaggle's online lab for GPU support, fine tuning, and efficient data handling, the model achieved an impressive 99.7% accuracy, reflected in its high F1 score. The successful implementation of this model significantly improved content moderation, providing an effective solution for detecting clickbait. By sharing the well explained code with the data science community, I contributed to collective knowledge, fostering learning and collaboration. This project not only refined my NLP and transformer model skills but also demonstrated the potential of AI in solving complex problems in digital content moderation.

Collaboration: AI Community at ISBAT University

Harm Prevention Natural Language Processing Generative AI Automation
Student Class Attendance System

Student Class Attendance System

The Student Class Attendance System project was developed as part of my Bachelor of Science in Artificial Intelligence and Machine Learning degree at ISBAT University. This project aimed to enhance attendance tracking in educational institutions by implementing an automated system that leverages deep learning and facial recognition technology. Traditional manual attendance methods are inefficient, time consuming, and prone to errors, making automation a necessary improvement. Motivated by the inefficiencies of current methods such as roll call, ID card scanning, and clickers, I designed a web-based application that performs real-time face recognition for attendance tracking. The system automates the identification process, ensuring accuracy and improving classroom control. The approach involved collecting student images, encoding facial features, and training a machine learning model capable of recognizing students in real-time. A web-based interface was developed to allow seamless interaction with the system. Several challenges were addressed, including data privacy concerns, the optimization of facial recognition models for accuracy, and the design of an intuitive and user-friendly interface. Ethical considerations in handling student images were prioritized, ensuring data security and compliance with privacy standards. The model was trained using state-of-the-art deep learning algorithms, refining its ability to recognize students under different environmental conditions. The project targeted educational institutions in Uganda, focusing on schools and universities with the infrastructure to support face recognition technology. By reducing administrative workload and minimizing attendance fraud, the system provided a reliable solution for attendance management. Real-time monitoring capabilities enhanced security, while the streamlined process allowed students and staff to focus on learning rather than administrative tasks. Ultimately, the implementation of this system contributed to improving attendance tracking, increasing efficiency, and fostering a more structured and engaging educational environment.

Artificial Intelligence Face Recognition AI in Education Monitoring System

My Career

Working Experience And Education Journey

Work Experience

Operation Team Member

Nobel Learning PBC

Remote

Nov 2024 - Present

Gaithersburg, MD, USA

  • Optimized database management processes, resulting in a 20% increase in system efficiency and a 15% reduction in data retrieval time.
  • Spearheaded compiler work to streamline code compilation procedures, leading to a 30% decrease in errors and a 25% improvement in overall software performance.
  • Resolved technical issues related to mail and discord management, achieving a 95% customer satisfaction rate and reducing troubleshooting time by 40%.
  • Managed key aspects of mastering operations, including quality control and process improvement, leading to a 10% decrease in defects and a 20% increase in overall product quality.
  • Coordinated international logistics for major events, facilitating opportunities for young leaders to network, share experiences, and build impactful cross-border partnerships.

Google

Apprenticeship

Remote

Sept 2024 - Feb 2025

  • Developed and implemented machine learning algorithms to optimize operational processes, resulting in a 15% reduction in production costs.
  • Analyzed data from advanced generative AI models to identify patterns and trends, leading to a 20% increase in predictive accuracy.
  • Collaborated with cross-functional teams to integrate Google Cloud technologies into existing systems, resulting in a 40% improvement in operational efficiency.
  • Certified Google Cloud Innovator

Machine Learning Engineer Intern

Nobel Learning PBC

Remote

Aug 2024 - Dec 2024

Gaithersburg, MD, USA

  • Specialising On Digital Transformation And Emerging Technologies
  • Engaged in Leadership Course and Practice sessions to enhance leadership abilities through practical exercises and real-world simulations, leading to a 30% improvement in team collaboration and decision-making.
  • Utilized AI chatbot technology to streamline customer service interactions, leading to a 25% decrease in response time and an increase in customer satisfaction by 15%.

Nobel Intern

Nobel Learning PBC

Remote

Jun 2024 - Aug 2024

Gaithersburg, MD, USA

  • Participated in the Nobel Internship Program, focusing on enhancing skills in machine learning.
  • Completed introductory courses covering Basics of Internet Troubleshooting, Pitch & Presentation, and Web Design.
  • Collaborated with international teams on machine learning projects, gaining hands-on experience.
  • Led and Facilitated Basics of Internet Troubleshooting in Advance leadership practice to enhance technical skills and communication abilities, leading to a 15% improvement in overall performance.

Professional Summary

Shongo On'osala Aaron

Results-driven Artificial Intelligent And Machine Learning Engineer, Data Scientist and Data Analyst with a strong foundation in mathematics and physics. Skilled in leveraging data-driven insights to solve complex challenges, specializing in Artificial Intelligence, Machine Learning, and Data Science with a proven track record of delivering impactful solutions through data analysis. Polyglot, with exceptional analytical and problem-solving abilities. Experienced in leading projects in Artificial Intelligence and data-driven fields, with a commitment to continuous learning and innovation.
  • Kampala, Uganda
  • +256 705445176
  • aarononosala@gmail.com

Education

Bachelor of Science In Artificial Intelligent And Machine Learning

May 2022 - Dec 2025

International Business, Science And Technology University

Kampala, Uganda

Artificial Intelligence Community

Co-Founder & Community Leader

Oct 2021 - Jan 2025

International Business, Science And Technology University

Kampala, Uganda

  • Established a vibrant AI community that connected over 200 students and professionals from diverse backgrounds, enabling them to collaborate on innovative projects and explore cutting-edge advancements in artificial intelligence.
  • Facilitated workshops and networking events, fostering skill development and collaboration within the AI field.
  • Guided community members in leveraging AI technologies to address real-world challenges, resulting in impactful solutions across various sectors.
  • Collaborated with university stakeholders to implement programs aimed at bridging educational gaps and improving opportunities for youth.
  • Trained and supported 50+ educators in adopting disruptive technologies, fostering a transformative shift in teaching practices.
  • Coordinated logistics for major events, enhancing students visibility and networking opportunities.

Higher Education Certificate In Information Technology

May 2021 - Feb 2022

International Business, Science And Technology University

Kampala, Uganda

Diplome D'Etat In Mathematics and Physics

Aug 2019

College Maele

Kisangani, DR. Congo

My Skills

Technical And Professional

Technical Skills

Python

99%

Pandas Numpy Sklearn

98%

Tableau Matplolib Seaborn

95%

TensorFlow Keras PyTorch

99%

Natural Language Processing

99%

Cloud Services

94%

Computer Vision

99%

Neural Networks

99%

Data Mining

98%

Data Wrangling

96%

Version Control

97%

Digital Image Processing

94%

Professional Skills

98% Artificial Intelligence
99% Machine Learning
99% Statics/ Math
96% Business Intelligence (BI)
99% Data Science
95% Predictive Modeling
99% Data Analysis
98% Domain Expertise
99% Generative AI
98% Problem Solving
99% Adaptability
100% Continuous Learing

Awards & Recognitions

Let Me Take You Through My Achievements

Matrix Representation

IJBEST - International Conference On “Impact of Disruptive Technologies on Business World: Challenges and Opportunities in Africa”

On 16th Nov, 2024

I had the privilege of presenting my research at the IJBEST International Conference on "Impact of Disruptive Technologies on Business World: Challenges and Opportunities in Africa." My paper, "Automated Feature Engineering and Optimal Feature Selection for Regression Models: A Case Study Using OpenFE," explored how Artificial Intelligence (AI) and Machine Learning (ML) are transforming data analysis, particularly in predictive modeling. Feature engineering and feature selection are essential in ML to improve model accuracy and efficiency. This study examined the effectiveness of automated feature engineering and selection in enhancing regression model performance using OpenFE. We applied our approach to predicting ground-level nitrogen dioxide (NOâ‚‚) concentrations using the GeoAI Ground-level NOâ‚‚ Estimation dataset, which integrates air quality monitoring station data with satellite-derived information on NOâ‚‚, precipitation, and temperature. This research offers valuable insights for data scientists and AI researchers seeking to optimize regression models through automated feature engineering and selection. It emphasizes the importance of balancing feature quantity and quality to build more robust and efficient predictive models.

GDG Osogbo

CV4A - Computer Vision for Agriculture Hackathon

On 29th May, 2024

I had an amazing experience at Makerere AI Lab in Kampala, Uganda, participating in the Computer Vision for Agriculture Hackathon, a four-week innovation challenge focused on transforming AIR Lab’s datasets into impactful AI solutions. Collaborating with AI researchers and data scientists, I applied machine learning to real-world agricultural problems. I was honored to receive a participation award, marking a rewarding milestone in my journey of leveraging AI for global impact.

GDG Osogbo

IJBEST International Conference - Acknowledgement

On 17th Nov, 2024

I was honored to receive two certificates for presenting my research papers at the IJBEST International Conference, along with a vote of thanks for my outstanding contributions. This recognition was a significant milestone in my career, solidifying my role as an Artificial Intelligence and Machine Learning Engineer and Researcher. It was incredibly rewarding to have my work acknowledged and to be part of such an impactful conference.

GDG Osogbo

Microsoft Responsible AI

On 4th April, 2024

I had a rewarding experience at Tech-hub Tinda, Kampala, Uganda, where I was selected to participate in the Microsoft Azure Responsible AI Workshop for Global Innovation Talents. This workshop was an eye-opening opportunity to explore the future of AI and gain hands-on experience in building trustworthy AI systems using traditional machine learning models and large language models (LLMs). Learning how to design AI solutions that are ethical, fair, and less harmful to society was truly valuable. Receiving the "Microsoft Azure Responsible AI Workshop" certification from Microsoft was a proud moment, reinforcing my commitment to developing responsible and impactful AI solutions as an Artificial Intelligence and Machine Learning Engineer.

GDG Osogbo

IJBEST - International Conference On “Impact of Disruptive Technologies on Business World: Challenges and Opportunities in Africa”

On 17th Nov, 2024

On the second day of the IJBEST International Conference, I had the honor of presenting my research paper, "Harnessing the Power of Advanced Deep Learning Techniques for Early Detection and Classification of Maize Leaf Diseases in Uganda." This study demonstrated the effectiveness of YOLOv8 in detecting and classifying maize leaf diseases. The model achieved an impressive accuracy, highlighting its potential to enable early disease detection, reduce agricultural losses, and enhance maize production. This research underscores the critical role of AI in agriculture, empowering farmers with timely, data-driven insights to optimize crop health management.

Live Demo - Student Class Attendance System, AI Innovations

On 19th Jan, 2024

A comprehensive look at the latest AI innovations driving change across industries and particularly the education sector, where class room control and student management is essential. This project address the existing problem of manual attendance system in educational organisations a cumbersome process, especially with a large number of students. It involves time-consuming processes like maintaining logbooks and records. By leveraging AI, this solution automates attendance tracking, eliminating the need for logbooks and manual record-keeping, making the process more efficient, accurate, and seamless.

Customer Support AI-Chatbot With Smart Recommendations System - The Future of AI Assistant

Published on 1st Mar, 2025

I developed a Next-Generation AI-powered customer support chatbot with an integrated recommendation system that suggests products along with their prices using AI automation incorporating Reinforcement learning from human feedback (RLHF) the latest, most prominent and advanced methods for fine-tuning large language models (LLMs). The second version of this chatbot with voice assistance capability, known as the AI-Driven Personalized Virtual Professional Engine, expands on its core functionality with an advanced project recommendation system. It delivers tailored career insights and dynamic professional profiles, offering a highly personalized experience. This enhanced version is deployed on my portfolio website, where it is available for use. Exploring the potential of AI to transform the way we live and work in the future.

Licenses & Certifications

My Commitment to Continuous Learing And Innovations

Featured
Prompt Engineering

Logo Prompt Design in Vertex AI Skill Badge

Google Cloud

Online

Completed the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discovered how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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Featured
AI Ethics

Logo Microsoft Azure Responsible AI

Microsoft Americas Azure Team

Online

Earners of the "Microsoft Azure Responsible AI Workshop" badge have implemented AI solutions with hands-on lessons using traditional machine learning models and Large Language Models (LLM) to produce AI systems that are less harmful to society and more trustworthy.

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Featured
Data Science

Logo Introduction to Data Science

Cisco Networking Academy

Online

Cisco verifies the earner of this badge successfully completed the Introduction to Data Science course. The holder of this student-level credential has a broad understanding in basic concepts of Data Analytics, Data Engineering, Data Science and AI/ML related job functions. They also have insight into opportunities available for pursuing career in various data roles.

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Featured
Machine Learning

Logo Machine Learning Operations (MLOps) for Generative AI

Google Cloud

Online

This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

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Featured
Generative AI

LogoGenerative AI Productivity Skills

Microsoft & LinkIn Learning

Online

Learn how to apply generative AI skills to your work to boost your productivity. Transform raw data into meaningful insights, create compelling presentations, and generate creative images using the latest tools.

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Featured
Data Analysis

Logo Data Analytics Essentials

Cisco Networking Academy

Online

Cisco verifies the earner of this badge successfully completed the Data Analytics Essentials course. The holder of this student-level credential has a broad understanding how the data analytics process creates value from data, can explain characteristics of data, including formats, availability and methods to acquire, can transform data and analyze data using basic statistical and data preparation techniques. The holder has completed hands-on activities using Excel, SQL, Tableau and other tools.

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Featured
Artificial Intelligence

Logo AI Fundamentals

DataCamp

Online

This course provides an intensive introduction to the core aspects of Articifial Intelligence, including strategy, leadership, and financial planning.

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Featured
AI Safety

Logo Responsible AI for Developers: Privacy & Safety

Google Cloud

Online

This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

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Featured
AI Reliability

Logo Responsible AI for Developers: Fairness & Bias

Google Cloud

Online

This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.

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Featured
Natural Language Processing

LogoLarge Language Models (LLMs) Concepts

DataCamp

Online

This course provides an intensive introduction to the core aspects of Large Language Models (LLMs), including architectures, training methodologies, optimization techniques, and explores model development strategies, the leadership role of LLMs in AI applications, and the efficient use of computational resources for training and deployment. By the end, learners will have a strong foundation in how LLMs function and how to manage their lifecycle effectively.

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Featured
AI Automation

LogoAutomating Tasks with Python

DeepLearing.AI

Online

This course covers the core aspects of automating tasks with Python, including scripting, workflow automation, and data processing. It explores techniques for writing efficient Python scripts, managing repetitive tasks, and optimizing resource utilization. Learners will gain hands-on experience in using Python libraries and frameworks to streamline processes and enhance productivity.

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Featured
Machine Learing & Data Science

Logo Feature Engineering

Google Cloud

Online

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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Testimonials

What People Say About Me & My Services

Aaron is an exceptional AI expert, delivering innovative solutions with precision, his service has been fantastic. Highly recommend to everyone!

★ ★ ★ ★ ★
User 1

Andrew Sachs

Founder & CEO, Nobel Learning PBC

Aaron combines deep AI expertise with a strong commitment to delivering impactful solutions. His work is consistently outstanding and highly reliable.

★ ★ ★ ★ ★
User 2

Laju Etchie

Responsible AI Engagement Manager, Google

Aaron is a highly skilled AI professional, consistently delivering innovative and effective solutions. His expertise and dedication are unmatched. 10/10 experience!

★ ★ ★ ★ ★
User 3

Nat Forgotson

CPO, Nobel Learing PBC

Aaron excels in AI and machine learning, providing innovative solutions that drive results. His expertise and work ethic are truly outstanding. Unmatched AI Engineer. We couldn't ask for a better partner!

★ ★ ★ ★ ★
User 4

Eric Jagwara

Zindi Africa, Coutry Ambassador - Uganda

Aaron is a talented AI professional, consistently delivering high-quality, innovative solutions. The team exceeded our expectations, providing timely and effective solutions!

★ ★ ★ ★ ★
User 5

Inobat Mirakhmedova

Operation Manager, Nobel Learing PBC

Aaron brings deep AI expertise and a proven track record of delivering results-driven solutions. Outstanding work, would definitely collaborate again in the future!

★ ★ ★ ★ ★
User 6

Douglas Onencan

AI & ML Engineer, Rota React

Get It Touch

Have a Project On Your Mind

Contact Me

Contact Me

Let's Work Together

For more detailed information about my services, projects, and expertise, whether you have inquiries, collaboration opportunities, hire me or would like to discuss how my skills align with your needs, I am available to connect and assist. Please book an appointment or contact me through my social channels below.

  • aarononosala@gmail.com
  • +256 705445176

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