To make machine learning (ML) repeatable and scalable, you need to invest in serving infrastructure (the “last mile”), ML operations, and governance, says Cloudera’s Sr. Product Manager Alex Breshears in the MIT-Cloudera webinar “How to Scale Production Machine Learning in the Enterprise.”
In the webinar, Breshears shared key challenges and lessons learned from Cloudera customers who have built large-scale production ML systems.
The webinar also featured Tom Davenport, a distinguished professor and author of several books including Competing on Analytics and The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.
Many organizations experimenting with AI and ML learn very quickly that ML models make up only a small fraction of real-world ML systems – the small black box in the middle of the diagram below, said Breshears, citing a diagram from a paper by Google researchers. Production ML requires a lot more.
Organizations intending to put ML into production and run it at scale need to invest in the following:
To achieve scale with ML and truly start reaping its benefits by embedding it everywhere, you will need to automate as many components in the ML development and deployment lifecycle as possible. While production ML projects are largely custom, a platform like Cloudera (and other tools – see “Want to Know More?”) can help you achieve that automation.
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