This is supporting slides to my demonstration on how to launch a data science service in the cloud.

1. Launch a Data Science
Environment in the Cloud
Created: Nicholas Toscano, MBA

2. Why Cloud Data Science Services?
Data scientists are expensive and in demand
• Building and managing environments is costly
Data science needs to be scalable and reproducible
• One-off environments cannot be capitalized on
Data science needs security and performance
• Data science works best on secure and performant data
and environments
Organizations need improved adoption
• Non-data scientists need to extend services and benefits
Organizations should move away from one-off data science environments

3. Reference Architecture
Oracle Cloud Infrastructure Data Science (OCI) Data Science
A fully managed, serverless platform for data science
teams to build, train, and manage machine learning
models.
• Data Science integrates with the rest of the OCI
stack:
• Including Oracle Functions, Data
Flow, Autonomous Data Warehouse, and
Object Storage.
• Oracle Accelerated Data Science (ADS) software
developer kit (SDK) is a Python library that's
included as part of the Data Science service:
• ADS has many functions and objects that
automate or simplify the steps in the data
science workflow.
Source: Oracle Architecture Center

4. Steps for Launching a New Service

5. Features and Benefits of Oracle Data Science Service
• Rapidly deployable
• Managed environments
• Scalable compute and storage
• Access data from any source
• Customizable storefront
• Cloud provided security
• Streamlined data science workflow
• Improve accessibility and adoption
Storefront Notebooks
REPRODUCIBILITY SECURITY PERFORMANCE ADOPTION

6. -End-