AI Productization Panel — Sean Naismith of Enova, Jason Smith of rMarkBio, Bich-Thuy Le of Allstate
AI is taking the enterprise world on a very fast ride. Companies who can utilize AI and ML to creatively leverage customer data into market trends will be the winners.
We explore how to go from model to product. Research and training models is only half the battle. To operationalize the AI and analytics insights for speed-to-market requires many specialized Machine Learning engineers in combination with time consuming deployment and validation process.
- Three major components that is required for AI to be successful
- Machine Learning Engineers – What are the four pillars/guiding principles one must have to deliver a successful AI initiatives? This is the underlying foundation for the entire project lifecycle
- Breakdown the 4 key stages: Model Deployment, Monitoring, Logging, and Test & Learn; provide some sample use cases for each of the 4 stages
- Continuous improvements
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