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Intermediate Course 2 of 3 · Board Infinity

MLOps and LLMOps: Deploying and Scaling AI in Production

Move AI systems from development to reliable production operation - CI/CD, LLMOps, evaluation, observability, tracing and drift monitoring, in four modules.

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2 weeks · 10 hours/week 4 modules · 16 assignments Intermediate · recommended experience English · updated April 2026
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Board Infinity Board InfinityInstructor · 287 courses on Coursera
ML/LLM CI/CD Architecture · 9:15

Pipeline to production

36 lessons from CI/CD to drift monitoring

Certificate included

Shareable on LinkedIn, your resume or CV

Assessed, not just watched

16 assignments across the four modules

6 Lakh+learners on our Coursera courses
10+domains covered
270+courses
57+specializations
01 About the course

From AI prototype to production AI system.

Prototype → CI/CD → evaluation → deployment → observability → continuous improvement.

4
Modules
Pipelines to observability
36
Video lessons
Plus 12 readings
16
Assignments
Four in every module
17h
Total learning time
≈ 2 weeks at 10 hours a week

MLOps and LLMOps: Deploying and Scaling AI in Production is the bridge between an AI prototype and a production AI system - for ML engineers, data scientists and software engineers who have to keep models and LLM applications running reliably.

You work through reproducible pipelines, versioning and CI/CD, then inference optimization - context windows, prompts, latency and token cost - then evaluation with LLM-as-a-Judge and quality gates.

It finishes with monitoring, tracing and drift detection using tools such as MLflow, Weights & Biases and Arize Phoenix. It's course 2 of 3 in the Managing AI Systems: Development, Deployment, and Governance specialization.

Inside the course
01Operationalizing AI pipelines≈ 5h 02LLMOps fundamentals≈ 5h 03Evaluation: from vibes to metrics≈ 4h 04Observability & tracing≈ 4h
02 Who it's for

For the people who have to keep AI running.

Basic familiarity with Python and ML concepts is recommended. No prior MLOps experience is required.

ML engineers

Pipelines that hold up

Production pipelines, deployment, monitoring and reliability for ML and LLM systems.

Data scientists

CI/CD from an ML-first view

Production deployment concepts without needing prior MLOps or DevOps experience.

Software engineers

Production AI architecture

Serving, CI/CD, reliability and observability for AI applications.

AI / platform

Platform professionals

Operationalizing models and LLM applications at scale.

03 What you'll learn

Four outcomes, in the order they build.

Build → deploy → evaluate → observe → improve → scale. These are Coursera's four official outcomes for the course.

Module 1

Configure CI/CD

Configure CI/CD pipelines for ML and LLM systems using GitHub Actions and MLflow.

Module 2

Optimize inference

Optimize LLM inference pipelines for reduced latency, token cost, and improved reliability.

Module 3

Evaluate

Build automated evaluation frameworks using LLM-as-a-Judge and quality gates.

Module 4

Observe

Instrument production AI systems with tracing, drift detection, and observability dashboards.

1:1 coaching · optional add-on

Take AI to production with someone in your corner.

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course. It's a separate, optional service - not part of Coursera enrollment.

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Get unstuck faster

Talk through the concept, error or trade-off that's slowing you down.

Your work reviewed

Feedback on what you build as you work through the four modules.

Practice beyond the quizzes

Work set for you, then reviewed, so the concepts turn into habits.

Aim it at a role

Which direction fits you - and what to learn next.

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04 Syllabus

Four modules, from pipelines to production.

Operationalize → optimize → evaluate → observe. Two weeks at 10 hours a week, self-paced. Every module lists its learning objectives, its content, and what you'll walk out with.

36 videos 12 readings 16 assignments 2 weeks · 10 h/week Download full syllabus
Learning objectives - by the end of this module you can:
  • Configure CI/CD pipelines for ML and LLM systems using GitHub Actions and MLflow
  • Integrate model and data validation into build → test → deploy
  • Version models, prompts and datasets, and use model registries with rollbacks and lineage
  • Keep environments reproducible with Docker, Conda, lockfiles and hashes, and promote them to production
What's inside
9 videos92 min
3 readings90 min
4 assignments105 min
1 plugin5 min
Topics covered
CI/CD architecture Build → test → deploy Model & data validation Semantic versioning Model registries Rollbacks & lineage Environment drift Docker & Conda Environment promotion
You'll leave with: a reproducible, versioned CI/CD pipeline for ML and LLM systems - with validation, registries and safe rollbacks.
Learning objectives - by the end of this module you can:
  • Optimize LLM inference pipelines for reduced latency, token cost and improved reliability
  • Design efficient context windows and structured prompts that reduce hallucination
  • Apply batching, caching, streaming and compression, and choose between API and local models
  • Log, track and scientifically evaluate prompt variants with W&B and MLflow
What's inside
9 videos78 min
3 readings90 min
4 assignments105 min
Topics covered
Context windows Structured prompts Hallucination reduction Latency budgets Token cost Batching & caching API vs local models Prompt variants Prompt evaluation
You'll leave with: a faster, cheaper, more reliable LLM pipeline - and a tracked record of which prompt variants work.
Learning objectives - by the end of this module you can:
  • Build automated evaluation frameworks using LLM-as-a-Judge and quality gates
  • Construct realistic evaluation data, sampling edge cases and avoiding bias
  • Design evaluator prompts that score consistency, relevance and correctness
  • Trigger evaluation during deployment and read dashboards for release readiness
What's inside
9 videos66 min
3 readings90 min
4 assignments75 min
Topics covered
Evaluation data Edge cases Test-data bias Evaluator prompts Automated scoring LLM-as-a-Judge Evaluation triggers Quality gates Release readiness
You'll leave with: an automated evaluation setup with quality gates that decide what ships.
Learning objectives - by the end of this module you can:
  • Instrument production AI systems with tracing, drift detection and observability dashboards
  • Log prompts, responses and metadata, compare experiments and track inference metrics
  • Trace chains and agents in Arize Phoenix to find hallucination triggers and bottlenecks
  • Tell data drift from behavioral drift, set up alerting, and decide when to retrain
What's inside
9 videos53 min
3 readings90 min
4 assignments105 min
Topics covered
Logging Experiment comparison Inference metrics How chains & agents break Tracing with Phoenix Hallucination triggers Data vs behavioral drift Drift alerting Retraining decisions
You'll leave with: a traced, monitored production system - and a clear rule for when to retrain or update it.
05 Tools & skills

The platforms you work with, and the skills you leave with.

The platforms Coursera's FAQ says you'll work with. Coursera's official tools list for the course is Model Deployment, Prompt Engineering and AI Workflows; Docker is also named in the FAQ.

Tracking & registry

MLflow

Model registries, CI/CD for ML, and logging prompt variants and experiments.

Experiments

Weights & Biases

Experiment tracking, comparing versions and evaluation automation templates.

Tracing

Arize Phoenix

Tracing execution steps, and tracing and debugging in module four.

CI/CD

GitHub Actions

Named in the official outcome for configuring CI/CD pipelines for ML and LLM systems.

Skills you leave with

The seventeen skills Coursera lists for this course, grouped the way the work splits - shipping it, running it, and building with LLMs.

Shipping it

Getting changes into production safely.

  • CI/CD
  • Continuous Deployment
  • Release Management
  • Application Deployment
  • Cloud Deployment
  • Containerization

Running it

Keeping it healthy once it's live.

  • Continuous Monitoring
  • Event Monitoring
  • Site Reliability Engineering
  • Scalability
  • AI Security

Building with LLMs

The models and applications being operated.

  • MLOps
  • LLM Application
  • Large Language Modeling
  • Retrieval-Augmented Generation
  • Model Training
  • Model Evaluation
Certificate

A credential you can share.

Finish the graded work and Board Infinity issues a shareable certificate through Coursera - with a verification link and a one-click path onto your LinkedIn profile.

A Coursera course certificate - not an accreditation, and not a certification from MLflow, W&B, Arize, GitHub, Docker or any other vendor.

On your LinkedIn profile

Coursera says the credential can be added to your LinkedIn profile.

Anyone can verify it

Its own verification link, so a hiring manager confirms it in seconds.

Earned, not attended

Awarded on a graded quiz in each of the four modules - it certifies what you can do.

Two names on it

Board Infinity as the provider, Coursera as the platform.

06 Career impact

Production AI needs operators - not just model builders.

Coursera's FAQ says the skills apply to ML engineering, AI platform and data engineering roles in production environments. These are capability areas, not guaranteed outcomes.

Where these skills apply

ML engineering

Pipelines, versioning, registries and release management for ML and LLM systems.

AI platform

Operationalizing models and LLM apps, with environments promoted to production.

Data engineering

Data validation in CI/CD, evaluation datasets and drift in production data.

AI evaluation

Quality metrics, LLM-as-a-Judge and quality gates that decide what ships.

Reliability & observability

Tracing, monitoring, drift detection, alerting and when to retrain.

LLM optimization

Latency budgets, token cost, batching, caching and reliable prompts.

The honest comparison

AI tutorials are everywhere. So what is this?

Free tutorials are a good way to learn individual techniques. What a production-focused course adds is the order, the connections between steps, and checking along the way.

What you get
This course
Disconnected AI tutorials
Structured progressionOperationalize → optimize → evaluate → observe
4 modules that build on each other
Topics in whatever order you find them
CI/CD and versioningModels, prompts and datasets
Validation, registries, rollbacks and lineage
Generic DevOps examples
Evaluation and quality gatesFrom vibes to metrics
LLM-as-a-Judge and release thresholds
Manual spot checks
Observability and driftAfter it ships
Tracing with Arize Phoenix, drift dashboards, alerting
Usually stops at the demo
Proof at the endSomething to show
Shareable certificate through Coursera
Nothing to show
07 The bigger path

Architect → Operationalize → Govern.

This is Course 2 of 3 in the Managing AI Systems: Development, Deployment, and Governance Specialization.

Course 1

AI Systems Design: RAG Pipelines and LLM Architecture

AI architecture, RAG, vector databases, orchestration and a functional Streamlit MVP.

≈ 17 hours
View course
You're here Course 2

MLOps and LLMOps: Deploying and Scaling AI in Production

CI/CD, LLMOps, evaluation, observability, tracing and drift monitoring.

≈ 17 hours · 4 modules
Back to top
Course 3

AI Risk and Compliance: Audit and Governance Foundations

The final step: governance, security and compliance for AI systems, including red-teaming.

≈ 17 hours
View course

Across the specialization, the applied project starts as a RAG-powered chatbot built with LangChain, ChromaDB and Streamlit. At this stage it is instrumented with tracing and evaluation, with test batteries to find latency and cost bottlenecks using Weights & Biases and Arize Phoenix. Not every part of the three-course project is completed in this course.

Know exactly what you're signing up for.

4 modules · 36 lessons · 16 assignments · free PDF

08 FAQ

Common questions

Answers follow Coursera's own FAQ for this course.

About the course

Basic familiarity with Python and ML concepts is recommended. No prior MLOps experience is required — the course builds from foundational CI/CD concepts.

You'll work with MLflow, Weights & Biases (W&B), Arize Phoenix, GitHub Actions, Docker, and various prompt engineering frameworks.

Yes. The course introduces CI/CD and deployment concepts from an ML-first perspective, making it accessible for data scientists.

Coursera states that the skills are directly applicable to ML engineering, AI platform, and data engineering roles in production environments. How you apply them depends on your role - completing the course does not guarantee any job outcome.

Enrollment & access

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Want someone building this with you?

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.

See 1:1 coaching

Take AI from pipeline to production.

MLOps and LLMOps, evaluation, observability and reliable deployment - four modules on Coursera.

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MLOps and LLMOps: Deploying and Scaling AI in Production · by Board Infinity · offered on Coursera

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