Climate & Agronomic Data Scientist at Syngenta
Serg Masís is a Data Scientist in agriculture with a lengthy background in entrepreneurship and web and mobile development, and the author of the bestselling book “Interpretable Machine Learning with Python”, and the upcoming book “DIY AI”. He’s passionate about data-driven decision-making, Responsible AI, behavioral economics, and making AI more accessible.
Watch live: March 7, 2023 @ 3:00 – 3:30 pm ET
QA for AI Systems
Trust is mission-critical for any technology, so if AI solutions are to supplant software, AI must reach the reliability standards currently expected from software. For that to happen, a new field of MLOps engineering has branched off from the DevOps. Also, Explainable AI (XAI) will be more widely adopted since it provides the toolset to interpret machine learning predictions and scrutinize metrics. To ensure increased reliability, and robustness new roles for Machine Learning Quality Assurance will appear likely within DevOps, SecOps, and MLOps teams, but also the roles of data scientist and Machine Learning engineer will evolve.
We will examine examples and discuss how they can revolutionize the way we train, but most importantly, evaluate and deploy machine learning models with examples from the agribusiness industry and the digital agronomy field.