MLOps Essentials: Model Deployment and Monitoring
by Kesha Williams · LinkedIn Learning
Our Verdict
Worth it — with caveatsMLOps Essentials: Model Deployment and Monitoring is a strong conceptual primer on the production side of MLOps, not a hands-on deployment course: in 1h 24m it cleanly maps continuous delivery, model serving, monitoring, drift management, and responsible AI, but teaches it all with slides and quizzes at a vendor-neutral level rather than with code. It is the second installment of a two-part LinkedIn Learning series by Kumaran Ponnambalam (Principal Engineer for AI at Cisco), following Model Development and Integration. It holds a real 4.5/5 from 298 ratings on LinkedIn Learning, with the most common critique being that it 'reads like a textbook' and ships without referential notebooks to practice alongside. Note that the directory metadata for this entry is inaccurate: the real instructor is Kumaran Ponnambalam (not Kesha Williams), the real length is ~84 minutes (not 2 hours), and Docker/CI-CD are discussed only conceptually under generic 'deployment pipelines' and 'tools and technologies,' never as hands-on exercises. Treat it as a fast mental-model builder you pair with a hands-on course, not a standalone path to deploying a model yourself.
It is an excellent, well-structured conceptual overview of production MLOps for the time invested, but it is theory-only with no coding labs, so its value depends entirely on whether you want vocabulary and frameworks (great fit) or hands-on deployment skills (wrong fit).
Best for: Practitioners and managers who already understand ML model development and want a fast, well-organized vocabulary and mental map of the production side: deployment rollout strategies, serving patterns and scaling, monitoring and observability, concept/feature drift, and responsible-AI concerns. It is ideal as exam-style or interview-prep grounding, as a manager's or data scientist's orientation to what an MLOps platform must cover, and as the second half of Ponnambalam's two-part series for someone who took (or understands) Model Development and Integration first.
Skip if: Anyone expecting hands-on, tool-specific skills. If you want to actually containerize a model with Docker, build a CI/CD pipeline, stand up a serving endpoint, or wire up drift detection in code, this course will frustrate you because it stays at the slide-and-concept level with no notebooks or step-by-step labs. Complete beginners with no prior ML model-building exposure should start one level down, and people seeking an accredited or employer-recognized credential should look elsewhere since LinkedIn Learning certificates carry limited formal weight.
About This Course
Deploy ML models to production and monitor performance covering Docker, CI/CD, model versioning, and drift detection.
What You'll Learn
Curriculum
Getting started with MLOps, course coverage, and a review of the MLOps lifecycle (3 videos).
An ML production setup, deployment pipelines, rollout strategies, planning for infrastructure, deployment best practices, and tools/technologies for deployment (6 videos).
Serving patterns, scaling model serving, building resiliency, serving multiple models, and serving tools/technologies (5 videos).
The monitoring pipeline, instrumentation for observability, metrics to monitor, ML production-data best practices, alerts and thresholds, and monitoring tools (6 videos).
Introduction to model drift, concept drift basics and management, feature drift basics and management (5 videos).
Elements of responsible AI, explainable AI, fairness in ML, security of ML assets, and privacy in machine learning (5 videos).
Continuing on with MLOps and next steps.
Prerequisites
- Working understanding of the ML model-development workflow (ideally LinkedIn Learning's 'MLOps Essentials: Model Development and Integration', the same author's part 1, 1h 36m)
- General familiarity with software deployment concepts (pipelines, environments, releases) helps but no coding is required to follow along
- A LinkedIn Learning subscription or active free trial to access the videos and certificate
Instructor
Kesha Williams
Instructor · LinkedIn Learning
Pros & Cons
Pros
- Tightly structured, comprehensive map of production MLOps (delivery, serving, monitoring, drift, responsible AI) delivered in under 90 minutes, with 6 quizzes to check understanding
- Credible instructor: Kumaran Ponnambalam is a Principal Engineer for AI at Cisco and a prolific LinkedIn Learning author with large reach (individual courses such as Business Analytics Foundations and Agentic AI for Developers have 120K+ and 91K+ viewers respectively), lending real practitioner authority
- Strong, balanced coverage of drift (both concept and feature drift) and a full Responsible-AI chapter (explainability, fairness, security, privacy) that many MLOps intros omit
- Vendor-neutral framing means the mental models transfer across cloud providers and toolchains rather than locking you into one platform
- Earns a shareable certificate and is fully accessible during LinkedIn Learning's one-month free trial, so it can be completed at zero cost
Cons
- No hands-on labs or code: multiple learners describe it as 'like reading a textbook,' noting it ships without referential notebooks to practice alongside the content
- Stays high-level on tooling: Docker, CI/CD, and model versioning are referenced only conceptually under 'deployment pipelines' and 'tools and technologies,' never demonstrated step by step
- Assumes prior ML-development knowledge and really functions as part 2 of a series, so it is not a self-contained starting point for newcomers
- LinkedIn Learning certificates are not accredited and carry limited weight with employers compared with project-based or specialized credentials
Alternatives To Consider
Frequently Asked Questions
Is MLOps Essentials: Model Deployment and Monitoring free?
MLOps Essentials: Model Deployment and Monitoring is $29.99/mo. Included with a LinkedIn Learning subscription (about $29.99/month or ~$19.99/month billed annually) and fully accessible during the standard one-month free trial, so it can be completed and certified for $0 if you cancel in time. Because the course is theory-only, learners wanting hands-on practice should budget for a complementary project-based course; the free MLOps Zoomcamp (DataTalks.Club) is a well-regarded hands-on, end-to-end option that pairs well with this conceptual primer.
Who is MLOps Essentials: Model Deployment and Monitoring for?
Practitioners and managers who already understand ML model development and want a fast, well-organized vocabulary and mental map of the production side: deployment rollout strategies, serving patterns and scaling, monitoring and observability, concept/feature drift, and responsible-AI concerns. It is ideal as exam-style or interview-prep grounding, as a manager's or data scientist's orientation to what an MLOps platform must cover, and as the second half of Ponnambalam's two-part series for someone who took (or understands) Model Development and Integration first.
What will you learn in MLOps Essentials: Model Deployment and Monitoring?
How a production ML setup differs from notebook development, and the role of continuous delivery, deployment pipelines, and rollout strategies (e.g. staged/canary-style releases); Model serving patterns: scaling, building resiliency, and serving multiple models, plus the categories of tooling used for each; How to design a monitoring pipeline with instrumentation/observability, which metrics to track, and how to set alerts and thresholds for ML in production; Concept drift vs feature drift: how to recognize each and approaches to manage and remediate model degradation over time.
What are the prerequisites for MLOps Essentials: Model Deployment and Monitoring?
Working understanding of the ML model-development workflow (ideally LinkedIn Learning's 'MLOps Essentials: Model Development and Integration', the same author's part 1, 1h 36m); General familiarity with software deployment concepts (pipelines, environments, releases) helps but no coding is required to follow along; A LinkedIn Learning subscription or active free trial to access the videos and certificate.
Is MLOps Essentials: Model Deployment and Monitoring worth it?
It is an excellent, well-structured conceptual overview of production MLOps for the time invested, but it is theory-only with no coding labs, so its value depends entirely on whether you want vocabulary and frameworks (great fit) or hands-on deployment skills (wrong fit).
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on LinkedIn Learning's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official course page - MLOps Essentials: Model Deployment and Monitoring (LinkedIn Learning)
- Part 1 / prerequisite - MLOps Essentials: Model Development and Integration (LinkedIn Learning)
- Instructor profile - Kumaran Ponnambalam (Principal Engineer for AI, Cisco)
- LinkedIn Learning cost, free trial, and certificate value context
- MLOps Zoomcamp - free hands-on MLOps course (alternative for practical depth)