Key requirements for an enterprise ML platform
To deliver the business values for ML at scale, organizations need to be able to experiment quickly with different scientific approaches, ML technologies, and datasets at scale. Once the ML models have been trained and validated, they need to be deployed to production with minimal friction. While there are similarities between a traditional enterprise software system and an ML platform, such as scalability and security, an enterprise ML platform poses many unique challenges, such as integrating with the data platform and high-performance computing infrastructure for large-scale model training. Now, let's talk about some specific enterprise ML platform requirements:
- Support for the end-to-end ML life cycle: An enterprise ML platform needs to support both data science experimentation and production-grade operations/deployments. In Chapter 8, Building a Data Science Environment Using AWS ML Services, we learned about the key...
