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Deploying Machine Learning Models to Production: Challenges & Solutions

In this presentation, you will look at the top challenges you face deploying machine learning models to production and how to tackle those challenges using MLOps.
Course info
Level
Intermediate
Updated
Oct 6, 2020
Duration
31m
Table of contents
Deploying Machine Learning Models to Production: Challenges & Solutions
Description
Course info
Level
Intermediate
Updated
Oct 6, 2020
Duration
31m
Description

For the most part, machine learning is similar to traditional software development and most of the principles and practices that apply to traditional software development also apply to machine learning. However, are certain unique challenges that come with deploying ML models to production. In this presentation, you will look at the top challenges you face deploying machine learning models to production and how to tackle those challenges using MLOps. Key takeaways include: How machine learning differs from traditional software development, the top challenges when deploying ML models to production, what MLOps is and how to tackle ML specific challenges, and anecdotes from deploying ML models using industry principles and best practices.

About the author
About the author

DevSecCon is the global community dedicated to DevSecOps to help implement security in the overall development process.

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