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The Challenges of Deploying (many!) ML Models

Posted Mar 10, 2023 | Views 532
# Challenges of Deploying ML Models
# Wallaroo
# Edge and ML
# Wallaroo.ai
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SPEAKERS
Jason McCampbell
Jason McCampbell
Jason McCampbell
Director of Architecture @ Wallaroo.ai

Jason McCampbell is the Director of Architecture at Wallaroo.ai and has over 20 years of experience designing and building high-performance and distributed systems. From semiconductor design to simulation, a common thread is that the tools have to be fast, use resources efficiently, and "just work" as critical business applications.

At Wallaroo, Jason is focused on solving the challenges of deploying AI models at scale, both in the data center and at "the edge". He has a degree in computer engineering as well as an MBA and is an alum of multiple early-stage ventures. Living in Austin, Jason enjoys spending time with his wife and two kids and cycling through the Hill Country.

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Jason McCampbell is the Director of Architecture at Wallaroo.ai and has over 20 years of experience designing and building high-performance and distributed systems. From semiconductor design to simulation, a common thread is that the tools have to be fast, use resources efficiently, and "just work" as critical business applications.

At Wallaroo, Jason is focused on solving the challenges of deploying AI models at scale, both in the data center and at "the edge". He has a degree in computer engineering as well as an MBA and is an alum of multiple early-stage ventures. Living in Austin, Jason enjoys spending time with his wife and two kids and cycling through the Hill Country.

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Demetrios Brinkmann
Demetrios Brinkmann
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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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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Abi Aryan
Abi Aryan
Abi Aryan
Machine Learning Engineer @ Independent Consultant

Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.

Prior to consulting, Abi was a visiting research scholar at UCLA working at the Cognitive Sciences Lab with Dr. Judea Pearl on developing intelligent agents and has authored research papers in AutoML and Reinforcement Learning (later accepted for poster presentation at AAAI 2020) and invited reviewer, area-chair and co-chair on multiple conferences including AABI 2023, PyData NYC ‘22, ACL ‘21, NeurIPS ‘18, PyData LA ‘18.

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Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.

Prior to consulting, Abi was a visiting research scholar at UCLA working at the Cognitive Sciences Lab with Dr. Judea Pearl on developing intelligent agents and has authored research papers in AutoML and Reinforcement Learning (later accepted for poster presentation at AAAI 2020) and invited reviewer, area-chair and co-chair on multiple conferences including AABI 2023, PyData NYC ‘22, ACL ‘21, NeurIPS ‘18, PyData LA ‘18.

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SUMMARY

In order to scale the number of models a team can manage, we need to automate the most common 90% of deployments to allow ops folks to focus on the challenging 10% and automate the monitoring of running models to reduce the per-model effort for data scientists. The challenging 10% of deployments will often be "edge" cases, whether CDN-style cloud-edge, local servers, or running on connected devices.

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