Merelda Wu
Lead Data Scientist @ Melio Consulting
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Merelda Wu:
Merelda is a Co-Founder and Lead Data Scientist at Melio Consulting. She is an AI evangelist who is deeply frustrated by the slow project lifecycle (from proof-of-concept to production). Her mission is to speed up the data science project lifecycle and to continuously deliver business value to her clients. She holds a BSc and MSc degree in Electrical Engineering from the University of Witwatersrand. She enjoys solving complex problems and subsequently scaling the solutions into production. Merelda also co-organises the Cloud Native Computing Foundation's Johannesburg chapter and is an avid meet-up speaker.
Harry Lee:
Harry is a DevOps engineer and evangelist with a strong background in the financial technology sector. His mission is to continuously deliver business value by ensuring high availability and scalability of the business services. His expertise is in designing and implementing cloud-native solutions, propagating the DevOps culture and providing DevOps training across the organisation.
Build with the end in mind: infrastructure-backed data science with Kubeflow
As data scientists, we usually prototype use cases and try to find the one that can generate business value with the data on hand. We jump straight to work and at the end of the PoC accidentally wow-ed the stakeholders so much that they want the solution in production tomorrow. We scramble around our Jupyter notebooks and scripts to put together a pipeline that we think is reliable, the infrastructure guy then turns around and says "I can't use any of this".
At our company, where we develop with deployment in mind with Kubeflow. From the beginning, infrastructure sits with data science to gather the requirements for production. We set up the Kubeflow pipeline to allow our experiments to run exactly as how it will be run in production. From the data scientist's perspective, it's the same as writing notebooks; from the infrastructure, it's the same as setting up Kubernetes.
In this talk, we will be presenting our data science workflow with Kubeflow both from the DevOp engineer's and data scientist's standpoints. We will also demonstrate how we have incorporated Kubeflow into our profile image analyser pipeline.
As the topic spans from infrastructure to data science, we believe there is a little bit of something for anyone in this talk - whether you are a data scientist, machine learning engineer, data engineer, infrastructure engineer or software engineer, as long as you are a cloud or data enthusiast!
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