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Many entities mistakenly consider ML models as the only significant challenge in an AI project. In reality, one of the most essential considerations for a successful AI/ML initiative is the infrastructure on which models are deployed, tested, and rolled out. This in itself creates the need for AI infrastructure specialists, which can be both difficult and costly to find. There is, however, another option: outsourcing your AI infrastructure to a multi-cloud managed service provider.
Choosing an open-sourced managed infrastructure solution can help you accelerate your time to production, easily and cost-effectively cover some essential skill gaps in your ML journey, and even increase your budget’s predictability. In this webinar, product managers Adrian Matei and Andreea Munteanu explore the implications and benefits of adopting a Managed AI solution like Canonical’s. Join us and find out more!
- Managed AI infrastructure overview & processes
- Open source products & platforms available to build your AI infrastructure
- Pre-deployment considerations
- Benefits of adopting the Managed AI solution