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Introducing MLOps: From Model Development to Deployment (AI)

A Practical Guide to Building, Automating, and Scaling Machine Learning Pipelines with Modern Tools and Best Practices

  1. Topics
  2. IT & Software
  3. MLOps & AI Deployment

Introducing MLOps: From Model Development to Deployment (AI)

InstructorSchool of AI
Duration1h 49m
Students28.5K
Rating4.5 (599)

More Related Topics

  • Machine Learning (ML)1368
  • MLflow for ML Lifecycle20
  • AI Model Deployment75
  • Azure Machine Learning36
  • AI-300 Exam Preparation4
  • CI/CD for ML Models42
  • AI Infrastructure Design30
  • DP-100 Exam Prep17
  • AI Engineering Masterclass76
  • AI Monitoring11
Price
$0.00
$17.99
Coupon
99/100 uses left
Last checked 3d ago
Access
Premium only
Upgrade to unlock this course deal

Coupon history

Comidoc has tracked 32 coupons for this course since 2025, last checked 2d ago. On average, a new coupon appears roughly every 10 days.

Coupon codeDiscountAddedStatusLifetime
JULFREE02100% offJul 3, 202605:23 PM UTCExpired~29 daysRan full term
JULFREE01100% offJul 3, 202605:17 PM UTCExpired~29 daysRan full term
JULFREE03100% offJul 3, 202602:05 PM UTCExpired~29 daysRan full term
JUNFREE01100% offJun 5, 202603:38 PM UTCExpired~27 daysRan full term
JUNFREE02100% offJun 4, 202602:44 AM UTCExpired~28 daysRan full term
JUNFREE03100% offJun 2, 202604:33 AM UTCExpired~30 daysRan full term
Comidoc Analysis

High-level overview of containerization and orchestration

Strengths

Foundational Tooling Exposure

The curriculum covers essential infrastructure components including Docker for containerization and Kubernetes for workload orchestration.

End-to-End Pipeline Workflow

Instruction spans the transition from data preparation through to model deployment.

More MLOps & AI Deployment courses

Complete MLOps Bootcamp With 10+ End To End ML ProjectsKrish Naik4.5 (4,235)50h 46m
Deployment of Machine Learning ModelsSoledad Galli4.5 (6,193)10h 27m
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7 Days of Hands-On AI Development Bootcamp and CertificationSchool of AI4.3 (403)9h 25m
Coupon
30 Projects in 30 days of AI Development BootcampSchool of AI4.3 (238)5h 39m
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TensorFlow: Basic to Advanced - 100 Projects in 100 DaysSchool of AI4.5 (220)5h 55m
AI Engineer Production Track: Deploy LLMs & Agents at ScaleLigency ​4.7 (2,896)18h 38m
Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD PipelinesGourav J. Shah4.4 (418)11h 34m
Coupon
AI Engineer Professional Certificate CourseSchool of AI4.5 (207)15h 24m

Limitations

Limited Technical Depth

Learners have noted a lack of detailed explanation regarding core programming concepts and specific tool implementations.

Incomplete MLOps Scope

Learners have noted a lack of detailed explanation regarding core programming concepts and specific tool implementations. One signal from late 2025 suggests these gaps exist, though the February 2026 update label does not prove a correction has been made.

Ambiguous Project Structure

The curriculum may omit critical production elements such as CI/CD and performance monitoring. A signal from late 2025 identifies these omissions, but the February 2026 update label does not prove a correction.

Best suited to

  • Data Scientists seeking a brief introduction to production workflows
  • Learners wanting a high-level overview of Docker and Kubernetes in an ML context

Less suited to

  • Those requiring deep technical explanations of programming concepts
  • Learners seeking comprehensive coverage of CI/CD or model monitoring
  • Individuals looking for highly detailed orchestration tutorials

Comidoc Score

4.9/10

Limited fit
Intermediate level

Comidoc verdict

This path moves from machine learning experimentation toward production via containerization and orchestration. It provides an introduction to setting up project structures with Git and deploying models locally using Kubernetes.

Instructional depth is a primary concern, as some sections lack sufficient conceptual explanation for core programming and tool implementations. Furthermore, essential MLOps pillars like CI/CD and monitoring are not explicitly addressed in the active learning path.

The course is best suited for learners who want a compact overview of how Docker and Kubernetes fit into an ML workflow without needing exhaustive detail.

Score breakdown

Curriculum depth
4.3

Learner signals suggest a lack of detail in tool implementation and missing topics like CI/CD.

Applied learning
5.0

The curriculum includes project-like tasks for structure and deployment, though their connectivity is unclear.

Clarity & experience
5.0

Instructional signals are mixed, ranging from easy-to-follow to lacking sufficient explanation.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
5.0

The curriculum aligns with the stated goals of transitioning models to production.