11-02, 16:05–16:45 (America/New_York), Winter Garden (Room 5412)
As data scientists, we are often unaware of what's happening under the hood when our models are put in the corporate cloud environment. If I had to deploy a model to the cloud provider from scratch, what are the options I have? This talk gives an introduction to basic cloud computing concepts and services so next time you will be confident in evaluating the different alternatives for taking your models to the cloud.
One of the pain points that tend to arise in different companies' teams of data scientists is the lack of basic cloud computing knowledge, which may leave us in a disadvantaged position when debating over how to implement a machine learning model in the cloud, or even when trying to debug what we have built and deployed.
This talk will be composed of 3 main pillars:
- Storage: where we'll go over the basics of object storage and databases in the cloud
- Compute: where we'll ellaborate on the distinction between serverless and managed, with a brief mention of container orchestration
- Monitoring: where we'll dive deep into how to monitor our software running in the cloud
All these pillars will be based on Amazon Web Services' offerings, mainly because of market share and secondly because of personal experience.
No previous knowledge expected
Born in Córdoba, Argentina. Started learning web development at the age of 13 and grew a love for machine learning back at 2018, after attending to my first PyData event at my home city. Currently working as a Senior Machine Learning Engineer for an american real estate company.