Oracle Cloud Infrastructure Data Science
In this course you will learn to rapidly build, train, deploy, and manage machine learning models using OCI Data Science. This is an OCI service on OCI for data scientists to collaborate on the development, deployment, and maintenance of machine learning models.
STUDENTS WILL LEARN TO
After completing this course, you will be able to:
- Identify OCI services to implement an ML solution for a business use case.
- Incorporate ML and cloud best practices.
- Use OCI Data Science to ingest, design, build, train, optimize, deploy, integrate, and maintain ML models.
- Apply OCI Data & AI services to create ML solutions.
About the course
COURSE TOPICS
Module 1 : Course Introduction
- Introduction
Module 2 : Introduction and Configuration
- OCI Data Science Introduction
- ADS SDK Overview
- Tenancy Configuration Basics
- Configure a Tenancy with OCI Resource Manager
- Networking for Data Science
- Authenticate to OCI APIs
Module 3 : Workspace Design and Setup
- How to Work with JupyterLab
- Conda Environment: Overview
- Data Science Conda Environments
- Manage Conda Environments
- OCI Vault – Introduction
- Using OCI Vault in OCI Data Science
- Code Repositories Git
Module 4 : Workspace Design and setup
- ML Lifecycle
- Access Data
- Data Preprocessing
- Introduction to Feature Types
- Custom Feature Types
- Data Visualization
- Model Training
- Oracle AutoML – Introduction
- Hyperparameter Tuning – ADSTuner
- Model Evaluation
- Model Explanations: Global Explainer
- Model Explanations: Local Explainer
- Model Catalog Overview
- Serialization and Managing Models
- Model Deployment
Module 5 : MLOps Practices
- MLOps Architecture
- Data Science Jobs
- Monitoring Logging
Module 6 : Related OCI Services
- Spark Applications, Data flow, and Data science
- Oracle Open Data
- OCI Data Labeling
- AI Perceptual Services
- AI Decision Services
Register for Training
Schedule
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