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Just Build It! Tips for Making ML Engineering and MLOps Real

Posted Jan 12, 2022 | Views 706
# NatWest Group
# Natwestgroup.com
# Industrial Data Science
# Machine Learning
# Operational Machine Learning Products
# ML
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speakers
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Andy McMahon
Director - Principal AI Engineer @ Barclays Bank

Andy is a Principal AI Engineer, working in the new AI Center of Excellence at Barclays Bank. Previously he was Head of MLOps for NatWest Group, where he led their MLOps Centre of Excellence and helped build out their MLOps platform and processes across the bank. Andy is also the author of Machine Learning Engineering with Python, a hands-on technical book published by Packt.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

Data science and Machine Learning in an industrial setting are hard. The problems you have to solve are complex, the data landscape is challenging and you often don't have the freedom you would like to design experiments or create observational studies on real-world processes. This is before you even think about how to manage stakeholders, use cloud technologies, write software or wrap your solution up into a product that has to run predictions 24/7/365 and support business operations! In this talk, we reflect on many of the learnings Andy has gained through the past few years working in successful data science and machine learning engineering teams building operational products that create millions of dollars of value. In particular, Andy discusses how he thinks we can 'bootstrap' ML Engineering (MLEng) and MLOps practices in your organization.

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