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Effective ML System Development

Posted Apr 12
# Organization and Processes
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SPEAKER
Leonard Aukea
Leonard Aukea
Leonard Aukea
Head of Machine Learning Engineering & Operations @ Volvo Cars

Leonard is driving ML Engineering and Operations at Volvo Cars. He is responsible for defining the overall mission and strategy for ML Engineering and Operations, leading the build of reproducible ML systems. Leonard Aukea has spent most of his career as a Data Scientist/ML Engineer.

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Leonard is driving ML Engineering and Operations at Volvo Cars. He is responsible for defining the overall mission and strategy for ML Engineering and Operations, leading the build of reproducible ML systems. Leonard Aukea has spent most of his career as a Data Scientist/ML Engineer.

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SUMMARY

In order to efficiently deliver and maintain ML systems, the adoption of MLOps practices is a must. In recent times, the ML community has embraced and modified ideas originating from software engineering with reasonable success. Software 2.0 (AI/ML) poses some additional challenges that we are still struggling with today. In addition to code, data and models also abide by the continuous principles (Continuous Integration, Delivery and Training). At Volvo Cars, we are embracing a git-centric, declarative approach to ML experimentation and delivery. The adoption of MLOps principles requires cultural transformation alongside supportive infrastructure & tooling that enables efficient development throughout the ML lifecycle. Join us for this session to learn about how Volvo Cars embraces MLOps.

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Posted Apr 12 | Views 17
# Feature Stores
# Production Use Case
# Systems and Architecture
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# Feature Stores
# Open Source
# Production Use Case
# Systems and Architecture