Niño R. Eclarin
Ninz is a machine learning engineer at an agri-tech company, with nearly 13 years of experience in the tech industry, specializing in machine learning, data science, and research. Their work primarily focuses on solving challenges in climate, agriculture, and biodiversity.
Beyond their professional role, Ninz actively supports tech and research initiatives behind the scenes. Whether working on projects over a good cup of coffee or riding trails high in the mountains, they are always engaged in innovation and exploration.
And above all, they have a deep love for custard buns.
Session
Given the rise of popularity of AI and machine learning, more and more applications are leveraging on these powerful tools. They open up so many possibilities in with proper integration to your system.
There are many aspects of AI and machine learning that can affect its performance such as quality of data, type of algorithm, structure of the models, and deployment environment. However, one important aspect is often overlooked and frowned upon.
Model maintenance and validation is often left on the sidelines as users of AI and ML often focus on the other aspects. Improving your data for example is a good way to increase model performance - on paper. But same as any other software application, any kind of ML model needs proper maintenance to make sure that its result is still valid and accurate over time.
This session will help us understand how to implement a process of validating and maintaining models using python. We will deal with concepts such as data drift and model drift. We will also implement and experiment on different validation approach.