Two main challenges are hindering the adoption of AI for enterprises and government agencies. The first is an increase in the need for hybrid solutions to manage data and data science applications, to address data locality in accordance with a rise in regulation and data privacy considerations. The second is an increase in first-hand experiences with the challenges and complexities involved in operationalizing machine learning, especially when considering hybrid deployment options, and when scaling data science across the organization. But good solutions exist to overcome these challenges — simple ways to work across hybrid cloud and edge environments without compromising on speed or performance.
Watch this session to explore:
- Some common applications for machine learning at the edge and the main challenges associated with deploying distributed cloud and edge applications
- What you can do to achieve the same simple experience and leverage the same APIs and tools across cloud and edge
- How to select which processes to run in the cloud and which at the edge – across training, production and management, including an example deployment architecture
- How to run a distributed cloud or edge application on Amazon Cloud and Outposts with the Iguazio Data Science Platform (live demo)
- Additional considerations for deploying AI in hybrid environments and common pitfalls to avoid
This post has been republished by AIIA. To view the original video, please click HERE.
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