Brackly Murunga
Portfolio Data Scientist @ M-KOPA
Nairobi, Kenya
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Brackly Murunga is a Machine learning researcher with a professional background in applied data science and machine learning with a career spanning half a decade in multiple industries including Tech, FMCG and Fintech.
He was the Lead AI/ML Engineer at Phindor LTD before transitioning to BAT Kenya as a data scientist. He currently is a portfolio data scientist at M-KOPA.
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Kubeflow Unleashed: Harnessing Open-Source MLOps for Scalable,Cost-Effective End-to-End AI Pipelines
Africa faces unique challenges in adopting DevOps especially in the wake of machine learning and AI —limited budgets, infrastructure constraints, and the need for secure, scalable solutions. Kubeflow offers a game-changing solution by providing an open-source MLOps platform that reduces costs, simplifies operations, and scales effortlessly with Kubernetes. It empowers African organizations to automate AI workflows, secure sensitive data, and deploy models at scale—all without expensive proprietary tools.
This session will explore how Kubeflow tackles these challenges, unlocking the potential for African innovators to build and manage AI pipelines that meet local needs and global standards, affordably and efficiently. Whether you a seasoned ML practitioner, DevOps guru or student looking to learn, this session will unpack Kubeflows A-Z to show you its potential and use cases.
Privacy-Preserving Framework for collaborative machine Learning on sensitive Data
Deep learning Algorithms are data-hungry, the more data they have the more they are able to generalize really well on unseen data. Even though, efforts have been put on gathering and publishing huge datasets for unsensitive data to achieve the aforementioned effect, the same cannot be done for sensitive data for obvious reasons. This has made development of large robust models in areas with sensitive data like finance, healthcare limited to large organizations with lots of data.
The alternative, sharing of data across healthcare/financial practitioners, could help with development of capable models due to the variety of rich data that they possess, however, data security and privacy concerns arise .
In my session I intend to showcase a framework for sharing sensitive information across organizations for collaborative training of one deep learning model in a privacy preserving way using autoencoding and differential privacy.
Brackly Murunga
Portfolio Data Scientist @ M-KOPA
Nairobi, Kenya
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