Oracle Cloud Infrastructure Data Science Professional Course Overview

Oracle Cloud Infrastructure Data Science Professional Course Overview

The Oracle Cloud Infrastructure Data Science Professional course is designed to equip learners with the necessary skills and knowledge to effectively utilize the OCI Data Science service for building, training, and managing machine learning models in the cloud. It covers an extensive range of topics, from the basics of setting up and configuring the environment, to advanced model deployment and MLOps practices.

Starting with an introduction to the platform in Module 1, the course progresses through initial configuration (Module 2), workspace design (Module 3), and the full machine learning lifecycle (Module 4), including data preprocessing, visualization, and model evaluation. It also delves into Oracle AutoML, feature engineering, and model explanations, providing a comprehensive understanding of how to create robust predictive models.

Module 5 focuses on MLOps architecture, demonstrating best practices for maintaining and monitoring machine learning projects, while Module 6 introduces related OCI services, enhancing learners' capabilities in data science with tools like Spark, Data Labeling, and AI Services.

For individuals and professionals looking to delve into the world of cloud-based data science, this course provides a solid foundation and a pathway to mastering Oracle Cloud Infrastructure's data science tools and services, ultimately leading to the creation of scalable and efficient machine learning solutions.

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  • Live Online Training (Duration : 16 Hours)
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  • Live Online Training (Duration : 16 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure that you have a successful learning experience in the Oracle Cloud Infrastructure Data Science Professional course, we recommend that you meet the following minimum prerequisites:


  • Basic understanding of cloud computing concepts and the Oracle Cloud Infrastructure (OCI).
  • Fundamental knowledge of data science and machine learning concepts.
  • Experience with Python programming, as it is commonly used in data science tasks.
  • Familiarity with using JupyterLab or Jupyter Notebooks for interactive coding sessions.
  • Understanding of basic command-line interface (CLI) operations, as they may be used for tenancy configuration and other tasks.
  • Awareness of Git and version control systems for code repository management.
  • An introductory level of knowledge about machine learning algorithms and model evaluation techniques.

These prerequisites are designed to ensure that you can follow the course content effectively and fully benefit from the training. If you are new to some of these areas, we recommend that you take introductory courses in these subjects before enrolling in the Oracle Cloud Infrastructure Data Science Professional course.


Target Audience for Oracle Cloud Infrastructure Data Science Professional

The Oracle Cloud Infrastructure Data Science Professional course equips learners with the skills to leverage OCI for data science projects.


  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • IT Professionals with a focus on data science
  • Cloud Infrastructure Engineers interested in data services
  • DevOps Engineers looking to specialize in MLOps
  • Data Science Managers and Team Leads
  • Software Developers interested in machine learning and data processing
  • Data Science Students and Academics
  • Technical Architects designing data science solutions
  • IT Project Managers overseeing data science projects
  • Business Intelligence Professionals
  • Data Engineers
  • AI/ML Product Managers
  • Cloud Solution Architects


Learning Objectives - What you will Learn in this Oracle Cloud Infrastructure Data Science Professional?

Introduction to Learning Outcomes

This course equips students with the knowledge to harness Oracle Cloud Infrastructure for data science, from setting up environments to deploying machine learning models and implementing MLOps practices.

Learning Objectives and Outcomes

  • Understand the fundamentals of the Oracle Cloud Infrastructure Data Science service and the Accelerated Data Science (ADS) SDK.
  • Configure tenancy for data science projects using OCI Resource Manager and understand networking requirements.
  • Efficiently work within the JupyterLab environment and manage Conda environments for Python projects.
  • Utilize OCI Vault for managing secrets and credentials within data science workflows.
  • Grasp the machine learning lifecycle, including data access, preprocessing, visualization, and model training with Oracle AutoML.
  • Master hyperparameter tuning using ADSTuner and evaluate machine learning models effectively.
  • Implement and interpret model explanations using global and local explainers to gain insights into model behavior.
  • Learn to serialize, manage, and catalog models within OCI for easy access and tracking.
  • Deploy trained models into production, understanding the full deployment process and best practices.
  • Incorporate MLOps architecture, manage data science jobs, and set up monitoring and logging for deployed models.

FAQ's


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It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
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Yes, course requiring practical include hands-on labs.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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Yes, Koenig Solutions is a Oracle Learning Partner
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.

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