Course Description

Prepare to pass exam: Oracle Cloud Infrastructure 2024 Data Science Professional SP (Spanish)

The Oracle Cloud Infrastructure Data Science Certified Professional certification is intended for data scientists and other professionals responsible for building data science solutions and managing the complete lifecycle of machine learning models. The certification validates a candidate’s ability to effectively identify OCI services used for implementing machine learning solutions for specific business use cases. It also validates a candidate’s ability to incorporate machine learning best practices.

To prepare for the Oracle Cloud Infrastructure 2024 Data Science Professional SP (Spanish) exam, focus on mastering data science workflows, model building, and machine learning techniques within Oracle Cloud. Gain hands-on experience with OCI Data Science services, including model training, deployment, and performance evaluation. Utilize official Spanish study guides, practice exams, and tutorials to enhance your understanding. Familiarize yourself with key data science concepts and best practices to ensure both exam success and practical expertise in the field.

Additionally, focus on integrating Oracle's data science tools with other cloud services, such as data storage and processing solutions. By combining theoretical knowledge with practical experience, you'll be well-equipped to handle real-world data challenges and excel in the certification exam.

Exam topics covered

OCI Data Science - Introduction & Configuration        

• Discuss OCI Data Science Overview & Concepts
  
• Discuss the capabilities of Accelerated Data Science(ADS) SDK
• Configure your tenancy for Data Science

Design and Set up OCI Data Science Workspace        

• Create and manage Projects and Notebook sessions
  
• Create and manage Conda environments
• Use OCI Vault to store credentials
• Configure and manage source code in Code Repositories (Git)

Implement end-to-end Machine Learning Lifecycle        

• Discuss ML Lifecycle Overview
  
• Use different data sources to fetch data.
• Explore and Prepare data
• Visualize and Profile data
• Create and train models using OCI and Open source Libraries
• Create and Use automated ML capability from Oracle AutoML
• Evaluate models
• Obtain Global & Local Model Explanations
• Manage models using Model Catalog
• Deploy & Invoke a Cataloged Model
• Discuss ADS and OCI Generative AI Integration
• Discuss LangChain Application deployment to Data Science.
• Discuss Operators (optional)
• Discuss AI Quick Actions

Apply MLOps Practices        

• Discuss OCI MLOps Architecture
  
• Create & Manage Jobs for custom tasks
• Scale with OCI Data Science
• Discuss Autoscaling Model deployment for Inference
• Monitor & Log using MLOps Practices
• Use Pipelines to automate machine learning workflow

 Use related OCI Services         

• Create and Manage Spark Applications using Data Flow and OCI Data Science
  
• Describe OCI Open Data Service
• Create and Export a Dataset using OCI Data Labeling

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