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Model Monitoring: The Million Dollar Problem, Model Monitoring: The Million Dollar Problem, Model Monitoring: The Million Dollar Problem, Model Monitoring: The Million Dollar Problem

Posted Nov 24
# Panel
# Presentation
# Monitoring
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SPEAKERS
Alexandre Domingues
Alexandre Domingues
Alexandre Domingues
Machine Learning Team Lead @ Loka

Alexandre Domingues is an ML Team Lead at Loka, He holds a Masters in Electrical and Computer Engineering and a Ph.D. in Biomedical Engineering. He loves the interface between engineering/ML and life sciences and finding elegant solutions to hard problems.

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Alexandre Domingues is an ML Team Lead at Loka, He holds a Masters in Electrical and Computer Engineering and a Ph.D. in Biomedical Engineering. He loves the interface between engineering/ML and life sciences and finding elegant solutions to hard problems.

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Diogo Oliveira
Diogo Oliveira
Diogo Oliveira
ML Engineer @ Loka

Diogo Oliveira is a Machine Learning Engineer at Loka. He has an MSC in Electrical and Computer Engineering majoring in Systems, Decision, and Control from Instituto Superior Técnico. He is passionate about data science and ML and loves learning new things. Outside work he loves gaming, surfing, hiking, krav maga, and riding his bike.

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Diogo Oliveira is a Machine Learning Engineer at Loka. He has an MSC in Electrical and Computer Engineering majoring in Systems, Decision, and Control from Instituto Superior Técnico. He is passionate about data science and ML and loves learning new things. Outside work he loves gaming, surfing, hiking, krav maga, and riding his bike.

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Nicolás Roldán
Nicolás Roldán
Nicolás Roldán
ML Engineer @ Loka

Nicolás Roldán is a Machine Learning Engineer at Loka. He has a Bachelor in Science in Biomedical Engineering with the main emphasis on Biomedical Signal and Image processing. Also, he holds the AWS Machine Learning Specialty Certification and has been working using AWS in different projects related to development, labeling, training, and deploying ML models. When he is not coding, he enjoys reading manga and playing souls like video games.

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Nicolás Roldán is a Machine Learning Engineer at Loka. He has a Bachelor in Science in Biomedical Engineering with the main emphasis on Biomedical Signal and Image processing. Also, he holds the AWS Machine Learning Specialty Certification and has been working using AWS in different projects related to development, labeling, training, and deploying ML models. When he is not coding, he enjoys reading manga and playing souls like video games.

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Bojan Ilijoski
Bojan Ilijoski
Bojan Ilijoski
Senior Data Science & ML Engineer @ Loka

Bojan Ilijoski is a Senior Machine Learning and Data Science engineer at Loka. He is also a Ph.D. student and a teaching and research assistant at SS. Syril and Methodius University. Bojan loves exercising his curiosity both mentally and physically. He’s a programming and sports lover, Pythonista and father, runner, and biker who’s interested in AI, algorithms, and HPC. When he’s offline, Bojan is probably out hiking or immersed in a game of backgammon at the local cafe.

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Bojan Ilijoski is a Senior Machine Learning and Data Science engineer at Loka. He is also a Ph.D. student and a teaching and research assistant at SS. Syril and Methodius University. Bojan loves exercising his curiosity both mentally and physically. He’s a programming and sports lover, Pythonista and father, runner, and biker who’s interested in AI, algorithms, and HPC. When he’s offline, Bojan is probably out hiking or immersed in a game of backgammon at the local cafe.

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SUMMARY

Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor., Model and data monitoring is a crucial part of a production ML system — a lot can go wrong: model drift, data anomalies, upstream data, or processing failures. And mistakes can be costly if ML drives mission-critical systems like recommendation systems, credit approval, or fraud detection, easily meaning millions of dollars lost for large organizations. So, why do so few companies have ML monitoring fully implemented? The Loka team works with venture-backed startups and innovation programs in enterprises to implement end-to-end ML systems and brings their diverse experience to this Meetup. In the Meetup, several of Loka’s MLOps experts provide a thorough overview of model monitoring, the benefits for your business, and reference architectures to get you started. They also provide an introduction to several managed and open source solutions including SageMaker Model Monitor and Great Expectations. Bonus material includes access to a hands-on exercise for learning SageMaker Model Monitor.

+ Read More

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