WGU D789 ITSW 6106
Exam Code: D789
The WGU D789 – Machine Learning Operations (MLOps) (ITSW 6106) course focuses on deploying, managing, and maintaining machine learning models in production environments. The course covers MLOps principles, model lifecycle management, automation, monitoring, and scaling of machine learning systems within modern software and cloud environments.
- Exam Breakdown
- Domain Breakdown
- Access Breakdown
Exam Format
- Exam Code: D789
- Certification Level: Graduate / Master’s level university course
- Exam Duration: Varies depending on assessment type
- Passing Score: Competent / Pass (WGU uses competency-based grading rather than numeric scores)
- Unscored Content: Not applicable — course completion is based on competency assessments.
Exam Details
- Question Types: Objective assessment questions, case studies, and scenario-based analysis
- Number of Questions: Varies depending on the assessment format
- Hands-On Questions: May include performance assessments requiring deployment workflows, monitoring strategies, or evaluation of machine learning pipelines in production environments.
Exam Policies
- Assessment Model: Competency-based evaluation (pass/fail).
- Proctoring: Objective assessments are usually conducted through online proctoring.
- Waiting Period: Students must review feedback and work with course instructors before attempting a retake.
- Retake Fee: Typically included within WGU tuition; additional approvals may be required for further attempts.
Certification Validity and Renewal
- Validity: Not applicable (this is a university course, not a professional certification).
- Renewal Options: None required once course competency is achieved.
Exam Fee
- Base Fee: Included in WGU tuition (no separate exam fee).
- Taxes: Not applicable.
- Students enrolled at Western Governors University pay a flat-rate tuition per term, which includes course assessments such as D789.
Prerequisites
There are no strict prerequisites for the D789 course. However, students are generally expected to have:- Foundational knowledge of machine learning concepts
- Experience with programming and software development
- Understanding of data pipelines and model development workflows
- Familiarity with cloud platforms and automation tools
Exam Topics
MLOps Foundations: Principles of operationalizing machine learning models in production environments.- Model Lifecycle Management: Managing development, training, deployment, and monitoring of machine learning models.
- Continuous Integration and Continuous Deployment (CI/CD): Automating machine learning workflows and model updates.
- Monitoring and Observability: Tracking model performance, drift detection, and reliability of machine learning systems.
- Infrastructure and Scalability: Deploying machine learning models in scalable cloud environments.
Intended Audience
The D789 Machine Learning Operations (MLOps) course is designed for students pursuing advanced IT or software engineering programs, including:- Machine Learning Engineers
- Data Engineers
- AI Engineers
- Software Engineers working with ML systems
- Data Scientists transitioning to production ML systems
Career Impact
Jobs You Can Get:
- Machine Learning Engineer, MLOps Engineer, AI Engineer, Data Engineer, AI Platform Engineer.
- U.S.: $120,000–$165,000 USD
- India: ₹14,00,000–₹32,00,000 INR
- United Kingdom: £60,000–£90,000 GBP
- UAE: 220,000–380,000 AED per year.
Why It’s Valuable:
- MLOps skills are critical for organizations deploying machine learning at scale, ensuring reliability, automation, and operational efficiency of AI systems.
Exam Mode
Assessments for WGU courses are conducted online and may include:- Online objective assessments with remote proctoring
- Performance-based project submissions evaluated by faculty
- Students complete assessments through the WGU online learning platform.
Exam Booking Link
- Assessments are scheduled through the WGU Student Portal once students complete course preparation and readiness assessments.
- WGU Student Portal:https://my.wgu.edu
Once you pass the exam
- The course is marked as “Competent” in the WGU academic record.
- Academic credit is applied toward the student’s degree program.
- Students continue to the next course in their degree plan.
Offers
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Top Reasons to Choose
WGU D789 ITSW 6106
Growing Demand for MLOps Professionals
Organizations deploying machine learning solutions require professionals who understand model deployment, monitoring, and lifecycle management. MLOps skills bridge the gap between data science experimentation and reliable production systems.Focus on Real-World AI Deployment
The course emphasizes practical concepts such as CI/CD pipelines for machine learning, automated model deployment, performance monitoring, and infrastructure management required for scalable AI systems.Essential Skills for Production AI Systems
MLOps expertise enables engineers to build reliable machine learning platforms, maintain model performance, manage data pipelines, and automate AI workflows in modern cloud and enterprise environments.Top Certifications
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FAQ
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What is the WGU D789 Machine Learning Operations (MLOps) course?
The WGU D789 Machine Learning Operations course teaches how to deploy, monitor, and maintain machine learning models in production environments. Students learn about automated ML pipelines, model lifecycle management, infrastructure scaling, and monitoring systems to ensure machine learning solutions remain reliable, scalable, and effective in real-world enterprise environments.
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Is WGU D789 a professional certification exam?
No. WGU D789 is an internal university course offered by Western Governors University as part of certain IT or software engineering programs. It is not an external industry certification exam. Completing the course demonstrates competency in MLOps concepts and contributes academic credit toward a degree.
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How are assessments conducted in the D789 course?
Assessments in the D789 course follow WGU’s competency-based model. Students may complete objective assessments that are online-proctored or performance assessments involving projects and scenario analysis. Passing requires demonstrating mastery of MLOps concepts such as model deployment, monitoring, and automation of machine learning workflows.
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What skills do students learn in the MLOps course?
Students learn how to operationalize machine learning systems, automate model deployment pipelines, monitor model performance, detect model drift, manage ML infrastructure, and implement continuous integration and continuous deployment processes for machine learning environments.
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Is the D789 MLOps course useful for AI and data engineering careers?
Yes. MLOps skills are highly valued as organizations scale machine learning solutions in production. Professionals who understand model lifecycle management, automated pipelines, monitoring systems, and scalable infrastructure are in strong demand across AI engineering, machine learning engineering, and data engineering roles worldwide.
