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Module 2: Advanced Data Science Governance
for Big Data, Machine Learning & AI

In this course, over 80 additional data science governance precepts and processes are described in relation to analytics platform governance and machine learning and AI pipeline governance stages.

A few examples of the many precepts and processes covered in this course are provided here, in relation to their corresponding analytics governance stages:

  • Ingress (including Source Data Access Constraints Assessment, Downstream Data Usage Analysis, etc.)
  • Data Lake (including Data Compression Template, Data Storage Lifecycle Definition, etc.)
  • Processing (including Processing Engine Standardization, Cluster Scaling Automation, etc.)
  • Data Warehouse (including On-demand Subject Access Request Compliance, SQL-based ML Model Version Control)

Below are further examples of the many precepts and processes covered in relation to corresponding machine learning and AI pipeline governance stages:

  • Problem Definition (including Adoption Risk Assessment, Project Budget Allocation)
  • Data Identification (including Organizational Data Regulations Adherence, Data Sensitivity Analysis, etc.)
  • Data Extraction (including Data Extraction Policy, Automated Data Access, etc.)
  • Exploratory Data Analysis (EDA) (including Data Discrepancy Notification, Summary Statistics Registration, etc.)
  • Data Validation (including Statistical Fingerprint Drift Threshold, Data Validation Logic Automation, etc.)
  • Data Preparation (including Feature Engineering Guidelines, Data Preparation Logic Unit Test Automation, etc.)
  • Model Training (including Algorithm Selection Criteria, Training Metrics Registration, etc.)
  • Model Testing (including Model Passing Threshold, Model Testing Automation, etc.)
  • Model Deployment (including Model Version Switching Rules, Model Execution Dependencies Assessment, etc.)
  • Model Monitoring (including Model Performance Degradation Notification, Model Performance Review, etc.)
  • Model Retraining (Model Retraining Triggers and Model Retraining Metadata Registration)

Relevant roles are also mapped to individual governance stages. For those also interested in completing the AI and Machine Specialist courses, mapping of the patterns from those courses to the governance stages is provided for reference purposes.

Taking the Course at a Workshop

This course can be taken as part of instructor-led workshops taught by Arcitura Certified Trainers. These workshops can be open for public registration or delivered privately for a specific organization. Certified Trainers can teach workshops in-person at a specific location or virtually using a video-enabled remote system, such as WebEx.

Visit the Workshop Calendar page to view the current calendar of public workshops.

Visit the Private Training page to learn more about Arcitura’s worldwide private workshop delivery options.

Taking the Course via an eLearning Study Kit

This course can be completed via self-study by purchasing an eLearning study kit subscription, which includes online video lessons, as well as online and offline access to the electronic course materials and additional supplements and resources designed for self-paced study and exam preparation.

Visit the eLearning Study Kits page for more information about eLearning study kits.

Visit the Arcitura online store for purchasing information.

Taking the Course via a Printed Study Kit

This course can be completed via self-study by purchasing a printed study kit, which includes the full-color course materials as well as additional supplements and resources designed specifically for self-paced study and exam preparation.

Visit the Printed Study Kits page for more information about full-color printed study kits.

Visit the Arcitura online store for purchasing information.

Certifications

This course is one of three courses that are used to prepare for Exam DG90.01. A passing grade on this exam is required to achieve the Data Science Governance Specialist certification.