Blog post by Levi Brackman on best practices for enterprise level data science. Data science, machine learning (ML) and artificial intelligence (AI) are relatively new endeavors for enterprise-level business. Many companies are batch training as well as batch scoring ML models.
Author gives a high-level overview of his experiences building an enterprise-level data science and ML/AI capability from the ground up.
The most important thing to realize when starting a large enterprise data science project that deploys AI is that the correct infrastructure needs to be in place or built out either on-premises or in the cloud.
The article describes:
- Motivation, challenges, and solutions for building enterprise scale ML / AI capability
- Infrastructure
- Talent (beyond data scientists)
- Enterprise Software
- Productionalization
- DevOps
- AI Evangelism
… and more. Good read!
[Read More]