I am a lover of all things Data and Tech, with a particular interest in Data Science, Artificial Intelligence and Software Development. In my current role, I am co-leading a team of specialist Data Scientists and Software Engineers, applying Machine Learning and Deep Learning techniques to data within a Bank. I am a firm believer and supporter of Open Source Technologies and scaled learning through MOOCs. I have experience taking Machine Learning and Deep Learning models to production by leveraging Open Source Technologies such as Redis, Kafka, Python and Docker.
I deliberately position myself at the intersection of technology and human connection because I am passionate about building relationships and smart Technologies. I strongly believe that Technology built without empathy is positively dangerous.
At my core, I have a deep-seated passion for Education and Youth Empowerment and am on a mission to democratize Tech Education.
Accepted Talks:
Getting Machine Learning Models Ready For Production Using Python
As a Scientist, it’s incredibly satisfying to be given the freedom to experiment by applying new research and rapidly prototyping. This satisfaction can be sustained quite well in a lab environment but can diminish quickly in a corporate environment. This is because of the underlying commercial value motive which science is driven by in a business setting — if it doesn’t add business value to employees or customers, there’s no place for it! Business value, however, goes beyond just being a nifty experiment which shows potential value to employees or customers. In the context of Machine Learning models, the only [business] valuable models, are models in Production!
In this talk, I will take the audience through the steps involved in moving from experiments in Jupyter Notebooks to automated model training, serving and deployments for Production using an array of Python tools such as Numpy, Pandas, SciKit Learn and Docker.
The intended audience for this talk includes Data Scientists, Software Engineers and any other Data practitioners who have or want to go through the journey of gaining real-time value from Machine Learning models in Production. This talk will impart lessons learnt in moving from Jupyter experiments to writing production-ready Python code, as well as impart important Python tools, frameworks and libraries which can be used to accelerate such a transition.
