1Z0-1110-25 Oracle Cloud 2025 Data Science Professional

This Learning Path is ideal for Data Scientists and Machine Learning/AI Engineers who wish to expand their skills in implementing end to end machine learning solutions. It also prepares you for the Oracle Cloud Infrastructure Data Science Professional Certification.

Prerequisites:
1) Proficiency in using Python for Data Science / Machine Learning
2) Knowledge of Data Science & Machine Learning open-source libraries and their applicability
3) One year or more of ML experience 4) Six months or more of hands-on experience with OCI

The Oracle Cloud Infrastructure (OCI) Data Science Professional course is designed to equip Data Scientists, Machine Learning/AI Engineers with the skills and knowledge to effectively utilize OCI's comprehensive suite of data science tools. Spanning the entire machine learning lifecycle, from establishing a data science workspace to deploying and overseeing machine learning models in real-world environments, this course enables participants to harness OCI's robust capabilities. They will learn to construct, train, and deploy machine learning models, apply MLOps best practices, and integrate other OCI services to optimize their data science workflows.

After completing this course, you should be able to:

• Determine the appropriate OCI services to implement Machine Learning solutions for business use cases.

• Configure and manage OCI Data Science workspaces and projects.

• Utilize Accelerated Data Science (ADS) SDK for streamlined data science processes.

• Implement end-to-end machine learning lifecycle, including data preparation, model training, evaluation, and deployment.

• Apply MLOps practices to automate and monitor machine learning workflows.

• Integrate with other OCI services such as OCI Vault, OCI Object Storage, OCI Generative AI, OCI Data Flow and OCI Data Labeling.

Benefits to you

You will learn and be ready to apply the following skills,

• OCI Data Science Environments: Configuration and management of workspaces and projects, ADS SDK utilization for streamlined processes.

• Machine Learning Lifecycle: Data preparation, model training, evaluation, and deployment.

• MLOps Practices: Automation and monitoring of machine learning workflows.

• OCI Service Integration: Integration with Vault, Object Storage, Generative AI, Data Flow, and Data Labeling.

• ML and Cloud Best Practices: Identification and application of best practices for robust, scalable solutions.

• ML Solutions for Business: Identification and use of OCI services for business-specific machine learning solutions.