On infrastructure for AI and ML: managing training data to data storage, cloud strategy and costs of developing ML models.
The following theme and topics will be covered during the 2019 edition:
- Share experience stories and case studies of data acquisition.
- Data storage case studies.
- Tools for AI, ML and Deep Learning. Here, we want to hear about third-party tools, existing tools, for AI/ML/DL in-house.
- Storing data on the cloud and cloud strategy.
- GPU versus CPU – when you do use either and why? How do the strengths and limitations of each play out for your use case?
- Cost of developing ML models: is there a quantifiable way of doing this?
- CapEx and OpEx for AI – have you worked on this? Share your insights with the community.