Managed Services
Understand the shift from hosted to managed services in cloud computing and how serverless technologies help data scientists deploy scalable machine learning models. Learn about trade-offs like iteration speed, latency, cost, and scale through practical AWS and GCP examples, enabling you to separate model training from deployment effectively.
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Since 2015, there’s been a movement in cloud computing to transition developers away from manually provisioning servers to using managed services that abstract away the concept of servers.
Benefits
The main benefit of this new paradigm is that developers can write code in a staging environment and then push code to production with minimal concerns about operational overhead. The infrastructure required to match the required workload can be automatically scaled as needed. This enables both engineers and data scientists to be more active in DevOps, because much of the operational concerns of the infrastructure are managed by the cloud provider.
| Infrastructure | Operations | Development | |
|---|---|---|---|
| Managed | ❌ |