Building a scalable and cost-effective AI solution, it is important to have a mix of off-the-shelf AI solutions, AI services offered in the cloud, and custom components built and deployed using cloud-based development services. Out of these three, while developing custom AI modules and components, infrastructure costs starts getting attention only when it starts facing the real user data and load in the production environments.
In this point of view session we have discussed the below pointers,
1. What are the key factors to consider while deploying custom AI modules in the cloud?
2. How to apply performance engineering techniques while using Machine Learning models in the cloud?
3. What are the differences in the approaches used while designing the deployment model for Traditional versus AI applications?
4. How to optimize infrastructure costs for custom AI components?