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• 🤖 I'm Brayan Kai, a Data Scientist and Technical Writer. I'm passionate about building impactful solutions and products with Machine Learning. I'm never tired of learning and helping other developers advance their skills. I love writing technical articles on Machine Learning and Artificial Inteligence related topics and contributing to open source projects

• 👨🏽‍💻 Currently, I'm working on a project with Omdena in patnership with Learnroll. In this project, we're helping develop a model that can address the issue of implicit biases in healthcare. I collaborate with a cross functional team to help develop an AI model that can accurately help in identifying and mitigating implicit bias for a specific gender bias for patient vignette types in ER scenarios.

• 👥 I'm currently the Technical Writing Channel Lead for Dev Carrers Community. I'm also on the core team for GDSC Kabarak University, leading the Technical Writing track. I also volunteer in the Open Source Community Africa, Nairobi.

• 🚀 I am very passionate about giving back to the community and sharing the knowledge and experiences I have over time as a Data Scientist.I have given talks & sessions in the following events:

  • CHAOSScon Africa
  • Conference for Open Source Coders, Users & Promoters
  • DroidCon KE
  • Dev Fest Uyo & Mombasa
  • I/O Extended events in Nairobi & GDSC Nairobi Edition.
  • Several Google Developer Students Club meetups including; Moi University, Kabarak Univesity, Kibabii University, University of Lay Adventist Kigali, KCA University, Egerton University, Mount Kenya University, Rift Valley Technical Training Institute, Meru University of Science and Technology, Daystar University & Coast Institute of Technology
  • Space Ya Tech Tech-Talks
  • ...See more

Let's connect here :

• Open Source Solutions for Responsible AI Mitigations and Tracking This is a tehnical talk on Responsible AI focused on fairness that I gave at the Africa's Talking Summit 23. I covered the different types of algorithmic overing why algorithmi harms are not disjoint. I then delved into a pratial demonstration sharing with the audiene how they an use the Know Your Data tool to understand their data. Answering questions like:; is thier data corrupted or sensitive. Does it have gaps and is it balane aross various attributes. Helping them build more fair Machine Learning Models.

• Unlocking Data Insights for Fair Machine Learning: Exploring Know Your Data. In this engaging talk, I delve into the world of dataset biases and their impact on machine learning models. I explored how biases can perpetuate stereotypes and hinder fairness, emphasizing the need to understand and address these issues. The revolutionary Know Your Data (KYD) tool took the spotlight as I showcased its capabilities in empowering ML practitioners to gain deep insights into their data. By leveraging KYD's features, attendees learned how to identify biases, improve data quality, and foster fairer and more equitable models. Through a practical case study, I demonstrated the step-by-step process of utilizing KYD to address fairness concerns, providing attendees with actionable strategies and techniques to enhance their machine learning workflows.

• Building Responsible AI: It's Not Rocket Science In this session hosted by GDSC University of lay adventist Kigali I covered the key aspects of ethical AI development. I explored Ethics, Data Ethics, and AI Ethics, discussing responsible AI best practices. Additionally, i shared with the atendees on recognizing biases in ML and categorizing AI models through taxonomy. The session aimed to equip participants with the knowledge and tools to develop AI systems that are transparent, fair, and accountable. By emphasizing responsible AI development, I aimed to build awareness and promote ethical practices in AI without unnecessary complexity.

• Building Trust in AI: The Importance of Explainability in Open Source Projects In this talk which I gave at CHAOSScon Africa 23 I dived into the critical role of explainability in open source AI and ML Projects and its impact on community health. Sharing how transparency and accountability in open source AI projects can foster trust and collaboration. Empowering the attendees on the importance of building trust in their AI projects through Explainability.

• Streamlining Data Analysis with Simple ML for Sheets In this session hosted by Space Ya Tech I talked how you can apply the power of machine learning to your data in Google Sheets in just a few clicks. I explored the following with live demos: Predicting missing Values, Spotting abnormal values, Forecasting Values, Training and understanding a model from your data. Atendees were able to learn how to leverage the add-on, Simple ML For Sheets, on Google Sheets to bring the power of Machine Learning to where there data is.

• A Gentle Introduction to Neural Networks In this session hosted by GDSC UNILAK, I covered an introductory session to the atendees about Neural Networks. Covering the essentials explaining the concepts of Neurons, common activation functions , layers and loss & Optimizer functions. I did a demo on building a simple Neuron Network and each participant was able to build their machine learning model with neural networks.

• Crowdsource Africa Day Meet-up Had a chance to give a talk at the Crowdsorce Africa Day Meet-up. This is a day where as Africans we celebrate our Unity , shared heritage and reaffirm our commitment in building a prosperous Africa. I shared about the Kenyan Coast where I specifically focused on our culture and the history of successes we have had as the coastal region of Kenya. Highlighting our Cultural Diversity and Swahili Heritage, Beautiful beaches and Marine life not forgetting our Historical and Architectural Significance.

• Machine Learning : Basics and Beyond In this session hosted by GDSC UNILAK, I walked the atendees through an intoduction to machine learning. I covered the concept of Rule based aaproach and Machine Learning approach in solving problems. Took the atendees through the Machine Learning Process from Idea to Implementation. Dived deep into the difference between Artificial Inteligence, Machine Learning and Deep Learning and the different Machine Learning problems i.e Classification, Regression, Clustering, Sequence Prediction and Style Transfer.

• GDG Nairobi Professional Machine Learning Engineer Study Group Being one of the facilitators for the Road to Google Developer Certification study group organized by GDG Nairobi. During the week 2 session, I led discussions on ML Problem Framing, which included translating business challenges into ML use cases, defining ML problems, establishing business success criteria, identifying risks, and envisioning future improvements. Furthermore, I guided participants through ML Solution Architecture, where we explored designing reliable and scalable ML solutions, selecting appropriate Google Cloud software and hardware components, and ensuring compliance with regulatory and security requirements. To support their exam preparation, I conducted a thorough review of sample questions for the Professional Machine Learning Engineer Certification.

• Open Source Day Nairobi Led a round table discussion on Open Source Contributions as Practitioners in the Data Field at the Open Source Day Nairobi. Here we discussed common challenges faced when contributing to data projects.We tackled common challenges, ensuring accuracy and quality, effective collaboration, and promoting inclusivity among underrepresented communities in the open source arena. It was an engaging exchange of ideas that propelled us towards advancing open source data projects.

• Exploring the Intersection of Machine Learning, TinyML, and Responsible Innovation In this workshop held by GDSC Moi University, I had the opportunity to cover a wide range of topics to provide participants with a comprehensive learning experience. The workshop began with an introduction to Machine Learning (ML) and Tiny ML, covering key concepts such as supervised and unsupervised learning, regression, classification, and exploring the potential applications of Tiny ML. Additionally, participants gained insights into the fundamentals of the Internet of Things (IoT) and its various use cases across industries like healthcare, transportation, and smart cities. Through a hands-on demo session, learners were able to develop machine learning models and deploy them on an IoT device, specifically the Arduino NANO RP2040, thereby gaining practical experience in ML and IoT integration. Furthermore, the workshop emphasized the importance of responsible innovation and ethics in AI, delving into the ethical considerations and potential societal impacts of AI technologies, equipping participants with knowledge and strategies to ensure ethical practices and mitigate any adverse effects. Overall, the workshop aimed to provide a holistic understanding of Machine Learning, Tiny ML, IoT, and ethical considerations, empowering participants with valuable insights into these cutting-edge technologies.

• A Gentle Introduction to Neural Networks
• Machine Learning: Basics and Beyond

• The Ultimate Guide To Getting Started In Data Science
• Mastering Python the Right Way
• Data Structures 101: Introduction to Data Structures and Algorithms
• Python 101: Introduction to Modern Python

• The Complete Guide to AIOps - Everything You Need to Know
• Data Analytics with Excel Pivot Tables
• Inclusion Diversity and Data Science
• My Data Career Diary: Google I/O 22 Extended Nairobi

• List Index Out of Range - Suluhisho kwa Kosa la Python