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

Name: Just Build It! Tips for Making ML Engineering and MLOps Real
Uploaded: 2022-01-12

Posted Jan 12, 2022 | Views 567

# NatWest Group

# Natwestgroup.com

# Industrial Data Science

# Machine Learning

# Operational Machine Learning Products

# ML

SPEAKERS

Andy McMahon

Head of MLOps @ NatWest Group

Andy is a machine learning engineer and data leader with a passion for delivering valuable solutions that are robust, reliable and scalable.

As Head of MLOps at NatWest Group, he is responsible for driving operational best practice for AI and ML products and services across the bank and runs the internal MLOps Centre of Excellence.

He has delivered high-value ML solutions across multiple industries and is a multi-award winning data practitioner and leader.

He is also the author of the popular technical book, Machine Learning Engineering with Python, which is a practical guide to building real solutions using the latest ML engineering and MLOps best practices.

+ Read More

Andy is a machine learning engineer and data leader with a passion for delivering valuable solutions that are robust, reliable and scalable.

As Head of MLOps at NatWest Group, he is responsible for driving operational best practice for AI and ML products and services across the bank and runs the internal MLOps Centre of Excellence.

He has delivered high-value ML solutions across multiple industries and is a multi-award winning data practitioner and leader.

+ Read More

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.

+ Read More

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.

+ Read More