MLOps
Develop the skills to design a repeatable machine-learning delivery path through a learning path centred on MLOps. Explore concepts, guided practice and an applied project.
Build practical MLOps skills.
Skills and knowledge developed in this course.
- Model lifecycle
- Validation
- Deployment and monitoring
- Pipeline exercise
- Applied project: Design a repeatable machine-learning delivery path
- MLOps — validation and review
About this course
This course develops practical capability in MLOps. Its course-specific content covers Model lifecycle, Validation, Deployment and monitoring, Pipeline exercise, followed by an applied project focused on design a repeatable machine-learning delivery path. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with MLOps or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of emerging & specialized technologies. Prior experience with the named product helps with the practical exercises.
Design a repeatable machine-learning delivery path
Deliver design a repeatable machine-learning delivery path. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Model lifecycle
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- Data, training and artifact stages
- Reproducibility and lineage
Practical exerciseApply model lifecycle to the design a repeatable machine-learning delivery path and capture the validation evidence.
02Validation
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- Data and model quality checks
- Evaluation thresholds and release criteria
Practical exerciseApply validation to the design a repeatable machine-learning delivery path and capture the validation evidence.
03Deployment and monitoring
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- Serving patterns and rollout
- Drift, latency and service monitoring
Practical exerciseApply deployment and monitoring to the design a repeatable machine-learning delivery path and capture the validation evidence.
04Pipeline exercise
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- Trace a model from versioned input to deployment
- Demonstrate a monitored rollback condition
Practical exerciseApply pipeline exercise to the design a repeatable machine-learning delivery path and capture the validation evidence.
05Applied project: Design a repeatable machine-learning delivery path
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- Define inputs, assumptions and acceptance criteria for: Design a repeatable machine-learning delivery path.
- Implement the project using model lifecycle, validation, deployment and monitoring, pipeline exercise.
Practical exerciseDesign a repeatable machine-learning delivery path.
06MLOps — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Model lifecycle · Validation
Establish the MLOps scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Deployment and monitoring · Pipeline exercise
Apply the core MLOps techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for design a repeatable machine-learning delivery path. - 04Learning milestone 4 In progress
Deliver the applied project
Complete design a repeatable machine-learning delivery path, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗