Course #1 of the MLOps Specialization

See the full MLOps field – before picking a tool or a direction.

Course #1 of MLOps Specialization

AI is reshaping every technical role. If you work in DevOps, system administration, software engineering, or IT management – MLOps is the most direct path to staying ahead. This course gives you the full picture of how ML systems work in production, explained for engineers, not data scientists.

  • Understand the complete MLOps field – not just one tool or platform
  • See how AI, ML, model lifecycle, CI/CD, monitoring, and security all connect
  • Work through real examples with a dedicated GitHub project you can reuse
  • Understand the MLOps job market and where your skills fit
  • Know exactly what to learn next in the specialization path
Enroll Now One-time payment · € 129,00 · 2 years full access with extension available
🎬
41 Lectures3+ Hours of Video
🛠️
6 Hands-On Demos5 Practical Quizzes
🏆
Certificate of CompletionShareable on LinkedIn & CV
💬
Lesson-level Q&ADedicated GitHub Project
🗺️
Full MLOps Field ObservabilityCourse #1 of MLOps Specialization

Sound familiar?

AI is changing what employers expect from every technical role, and fast.

Your IT skills are solid. You know infrastructure, you’ve shipped code, you understand how systems run. But everywhere you look, job postings mention MLOps. Your team is deploying ML models. Someone has to understand how they actually work in production. That someone could be you.

🤔
“What even is MLOps, exactly?”
You’ve heard the term dozens of times. Every definition you find either oversimplifies it or drowns you in jargon. You need a clear map of the whole field – not a deep dive into one tool.
📉
“Everyone else seems to already know this.”
DevOps is maturing. ML is everywhere. The gap between what you know and what the market wants is growing, and you can feel it. A well-timed course can close that gap in weeks, not months.
🔧
“Every course just teaches one tool.”
MLflow. Kubeflow. SageMaker. There are endless tools, but none of the courses explain how they all fit together. You need the big picture first, then the tools make sense.
This course was built to solve exactly that. MLOps Fundamentals gives you a complete, tool-agnostic overview of the entire MLOps field – using real examples, real systems, and a real GitHub project. By the end, you’ll know how every piece connects, and you’ll be ready to go deeper in any direction.

What makes this different

Not another tool tutorial.
A full map of the MLOps field.

Most MLOps courses teach you one platform — SageMaker, MLflow, Kubeflow. This course teaches you how the whole field works, so every tool you encounter later makes sense immediately.

✓

Full-field observability, not tool lock-in

You’ll understand every domain of MLOps – data pipelines, model lifecycle, CI/CD, infrastructure, monitoring, security, cost, and how they connect to each other.

✓

Real systems, real examples

Not abstract diagrams. Every concept is illustrated with concrete, working systems you can reference. A dedicated GitHub project gives you code to explore and reuse.

✓

Built for IT professionals, not data scientists

Assumes you know infrastructure and code – not that you know how to train a neural network. No ML math, no Python data science prerequisites.

✓

A career map, not just a course

After MLOps Fundamentals, you know exactly which direction to specialize in – CI/CD, Model Lifecycle, Observability, FinOps, and which course takes you there.

✓

MLOps job market overview included

Understand what MLOps Engineers actually do, what roles exist, and what companies look for, including a real analysis of MLOps job requirements.

✓

Taught by a practitioner who teaches at university level

Alex has delivered Cloud & DevOps education at Master’s Degree level. He explains complex concepts clearly – no rushing through topics, no shallow explanations.

❌ Other courses

—Teach you to click through one specific platform
—Assume you want to become a data scientist
—Leave you confused about how everything fits together
—No context on careers, roles, or market demand

✓ MLOps Fundamentals

→Teaches the full MLOps landscape with real examples
→Built for DevOps engineers, developers, and IT professionals
→Shows how every piece connects – data, models, CI/CD, infra, monitoring
→Includes MLOps job market analysis and career path guidance

Learning outcomes

By the end of this course, you’ll be able to:

Six concrete skills you’ll carry into every MLOps conversation, interview, and role from day one.

🗺️ MLOps Concepts
✓
Explain what MLOps is and how it differs from Machine Learning and DevOps
Speak confidently about the MLOps field in team discussions, job interviews, and technical evaluations – without guessing.
🤖 ML Models
✓
Understand the end-to-end ML model lifecycle – from data to production
Trace how a model moves from experiment to deployment, and know where it can fail at each stage – before it fails in production.
⚙️ CI/CD
✓
Explain how CI/CD pipelines work for ML systems – and how they differ from standard DevOps pipelines
Understand what changes when ML enters the pipeline: training runs, model validation, registry promotion, and automated retraining.
🏗️ Infrastructure
✓
Understand how ML infrastructure is built with Docker, Kubernetes, and cloud platforms
Read and contribute to MLOps architecture discussions – know what containers, orchestration, and cloud services are doing in an ML context.
📊 Observability
✓
Understand why monitoring and observability are critical for ML models in production
Know what model drift is, why models degrade over time, and what an MLOps engineer does when a production model starts behaving unexpectedly.
🎯 Career
✓
Understand the MLOps Engineer role – responsibilities, career path, and what employers actually look for
Leave with a clear picture of where MLOps sits in the job market, what skills are in demand, and exactly which direction in the specialization to pursue next.

Each outcome maps directly to a course module – see exactly how in the curriculum below.

View full curriculum ↓

What you’ll learn

Course Curriculum

7 Modules
41 Lectures
3h 10m Video
6 Hands-on Labs
6 Quizzes
🗺️ 01
Introduction 11 lessons 🛠️ Includes hands-on lab
+
  • 📹 Course Introduction
  • 📹 About Your Instructor
  • 📹 Course Structure
  • 📹 What is Artificial Intelligence
  • 📹 What is Machine Learning
  • 📹 What is Deep Learning
  • 📹 What is MLOps and why it matters
  • 📹 Programming Languages for MLOps
  • 🛠️ Your First MLOps Project — Hands-on Lab
  • 📹 GitHub Repository with MLOps Project
  • 📝 Quiz — Introduction
🤖 02
ML Models 9 lessons 🛠️ 2 hands-on labs
+
  • 📹 ML Models vs ML Algorithms
  • 📹 Random Forest Classifier
  • 🛠️ Configuring Random Forest on Titanic Dataset — Hands-on Lab
  • 📹 Data for MLOps
  • 🛠️ Training Random Forest on Titanic Dataset — Hands-on Lab
  • 📹 ML Models Lifecycle
  • 📹 MLOps Tools for ML Model Lifecycle
  • 🔍 Deep Dive into ML Models
  • 📝 Quiz — ML Models
⚙️ 03
CI/CD for MLOps 7 lessons 🛠️ Includes hands-on lab
+
  • 📹 What is CI/CD
  • 📹 Core Principles of CI/CD Pipeline
  • 📹 MLOps vs DevOps Lifecycle
  • 📹 MLOps Tools for CI/CD
  • 🛠️ GitHub Actions Pipeline for Random Forest — Hands-on Lab
  • 🔍 Deep Dive into CI/CD for MLOps
  • 📝 Quiz — CI/CD for MLOps
🏗️ 04
Infrastructure for MLOps 7 lessons 🛠️ 2 hands-on labs
+
  • 📹 Overview of ML Infrastructure
  • 🛠️ Build Docker Image for Random Forest — Hands-on Lab
  • 🛠️ Deploy to Kubernetes for Random Forest — Hands-on Lab
  • 📹 Infrastructure Management
  • 📹 Cloud Providers
  • 🔍 Deep Dive into Infrastructure for MLOps
  • 📝 Quiz — Infrastructure for MLOps
📊 05
Observability for MLOps 5 lessons 🛠️ Includes hands-on lab
+
  • 📹 Observability for ML
  • 📹 Observability Tools for MLOps
  • 🛠️ Observability for Random Forest — Hands-on Lab
  • 🔍 Deep Dive into Observability for MLOps
  • 📝 Quiz — Observability for MLOps
📚 06
Additional Topics 2 lessons Field overview — Security, FinOps
+
  • 📹 Overview of Security & Compliance
  • 📹 Overview of FinOps
🎯 07
Career in MLOps 6 lessons Roles, salaries, roadmap, next steps
+
  • 📹 Key Players in AI/ML Teams
  • 📹 Core Responsibilities of an MLOps Engineer
  • 📹 MLOps Engineer Roadmap
  • 📹 MLOps Job Descriptions
  • 📹 MLOps Salary
  • 📹 Your Next Steps in the MLOps Specialization
🏁
Capstone Project with Instructor Review
Apply everything from the course to a real MLOps scenario. Your project is reviewed – with personal feedback on your implementation.
Included

Is this course right for you?

Built for IT professionals
ready to enter the MLOps field

You don’t need a data science background. You need IT experience and the ambition to grow into one of the most in-demand roles in tech.

⚙️
DevOps Engineers
Most direct path to MLOps
You already understand CI/CD, containers, and infrastructure. MLOps is DevOps applied to machine learning — and it pays significantly more. The transition is shorter than you think.
→ Your existing skills transfer directly. This course shows you exactly what’s new and different in the ML world.
🖥️
System Administrators
From ops to AI infrastructure
Deep knowledge of servers, networking, and cloud gives you a strong foundation. As companies scale their ML systems, they need engineers who can manage the infrastructure side — that’s you, upgraded.
→ Learn how ML models behave differently in production and what new operational challenges they create.
💻
Software Engineers & ML Developers
Build better ML systems
Whether you’re writing code alongside ML teams or building ML models yourself, understanding how models are deployed, versioned, monitored, and retrained makes you a significantly more valuable engineer.
→ Understand what happens after model training — and build systems that actually work in production.
📋
IT Project Managers
Lead AI/ML projects with confidence
If you’re managing teams delivering AI/ML systems, understanding what MLOps involves helps you estimate timelines, identify risks, and have more credible technical conversations with your team.
→ No need to go deep on implementation — the field overview and architecture clarity alone will improve your delivery.

Prerequisites — what you need before starting:

✓ You need
✓ Linux basics (terminal, file system)
✓ Basics of Git (clone, commit, push)
✓ Basic software development concepts (Python helpful, not required)
✗ You do NOT need
✗ Data science or statistics background
✗ Experience training ML models
✗ Prior MLOps knowledge
✗ Math beyond basic programming logic

MLOps Specialization path

This is Course #1 of the full
MLOps Specialization

Each course builds on the previous one. Start here to get your field overview – then go as deep as your career requires.

1
▶ You are here – Available now
MLOps Fundamentals Current Course
Full-field observability: AI/ML landscape, model lifecycle, CI/CD, infrastructure, monitoring, security, cost, and career paths – with real examples and a GitHub project.
Enroll Now
2
Available now
Python for MLOps
Practical Python skills for MLOps contexts – scripting pipelines, working with APIs, handling data, and automating ML workflows. No data science prerequisites
Enroll → View course
3
Available now
Machine Learning Algorithms for MLOps
The most widely used ML algorithms explained for engineers who operate them, not develop them – behavior in production, resource footprint, and deployment characteristics for each model type.
Enroll → View course
4
Coming soon
Machine Learning Model Lifecycle
From experiment to production: model versioning, registry management, staging environments, promotion gates, drift detection, and automated retraining pipelines.
🔔 Notify me
5
Coming soon
CI/CD for MLOps
Build ML pipelines from scratch: data CI, training CI, model packaging, container builds, registry promotion, deployment pipelines, safe rollouts, and LLM CI/CD – validated against 1k+ real job descriptions.
🔔 Notify me
6
Coming soon
Data for MLOps
Data pipelines, quality contracts, versioning, lineage tracking, and feature stores – the data infrastructure layer that makes ML models reliable in production.
🔔 Notify me
7
Coming soon
MLOps Observability & Monitoring
Production monitoring for ML systems: model performance dashboards, drift detection, alerting strategies, SLOs for ML services, and incident response for model failures.
🔔 Notify me
8
Coming soon
MLOps Security & Compliance
Supply chain security for ML systems, model governance, access control, compliance frameworks, and SecOps practices applied to ML infrastructure and pipelines.
🔔 Notify me
🎓
Specialization goal
MLOps Specialization Certificate
Nubes Academy is developing university accreditation partnerships. The specialization is designed to qualify as a recognized credential – not just another online certificate.

Start with MLOps Fundamentals and decide your next step based on your career goals.

Start with Course #1 – Enroll Now

What engineers say after completing this course

⭐⭐⭐⭐⭐   Verified student reviews – 100% five-star

  • 5-star student review screenshot 🔍 View
  • 5-star student review screenshot 🔍 View
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✕ ‹
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Enroll today

Everything you need to start your MLOps journey

One payment. 2 years full access with extension available. A structured path from MLOps beginner to confident practitioner.

  • 🎬
    41 Lectures · 3h 10min of video Stream on any device, any time. Pause and rewatch at your pace.
  • 🛠️
    6 Hands-on demos · 6 Practical quizzes Concepts applied to real systems with screen recordings.
  • 💻
    Dedicated GitHub project A real codebase to explore, clone, and reuse in your own work.
  • 🏁
    Capstone project with instructor review Personal feedback from Alex on your final implementation.
  • 💬
    Lesson-level Q&A access Ask questions on any lesson — answered by the instructor.
  • 🌐
    English subtitles Full subtitle support for non-native English speakers.
  • 🏆
    Certificate of Completion Share to LinkedIn and CV. Step 1 of the MLOps Specialization credential path.
MLOps Fundamentals · Course #1
€ 129,00
One-time payment  ·  2 years access
ℹ️ Price shown excludes VAT. Applicable VAT is calculated and added automatically at checkout based on your location.
Enroll Now – Get Instant Access
Secure checkout · Instant access after payment
  • ✓ 41 lectures · 3h 10min video
  • ✓ 6 hands-on demos
  • ✓ GitHub project + capstone
  • ✓ Certificate of Completion
  • ✓ Lesson-level Q&A
  • ✓ 2 years access
MLOps Fundamentals Certificate of Completion - Nubes Academy

Course credential

Certificate of Completion

Complete the course and earn a Certificate of Completion in MLOps Fundamentals – shareable directly to your LinkedIn profile and CV. For engineers building a case for a role change or promotion, a verifiable credential in a high-demand field carries real weight.

This certificate is also Step 1 of the MLOps Specialization path. Nubes Academy is developing university accreditation partnerships for the full specialization – meaning the credential you start building today is designed to grow into something recognized beyond a standard online course certificate.

🎓 Part of the MLOps Specialization path Complete all courses in the specialization to qualify for the full Specialization Certificate – designed toward university accreditation.
Alex Pototskyy - Founder, Nubes Academy

Your instructor

About Your Instructor

Alex is an Independent Consultant with 20+ years of experience across Software development, DevOps, Architecture, and technical leadership. He developed and delivered Cloud & DevOps curriculum at Master’s Degree level at a university, and continues to work as an active practitioner alongside building Nubes Academy.

What makes Alex’s approach different: his courses are built from data, not assumptions. The MLOps Fundamentals curriculum was designed by analysing what MLOps engineers actually do in production, not by following a generic textbook outline. The result is a course that covers what your future employer expects, explained in a way that makes sense if you already think like an engineer.

If you have a background in IT, and you want to understand where ML fits into the systems you already work with – this course was built for exactly where you are.

LinkedIn GitHub YouTube

Got questions?

Frequently asked questions

No. This course is designed for engineers with IT, DevOps, or software development backgrounds. You’ll learn what you need to know about AI and ML in the context of operating these systems — not building them from scratch. No statistics, no ML theory, no data science prerequisites.
The course includes 41 lectures and 3+ hours of video content, plus 6 hands-on demos and 5 practical quizzes. Most students complete it in 2–3 weeks at their own pace. You get lifetime access, so you can also work through it module by module alongside your daily job.
Yes — you receive a Nubes Academy Certificate of Completion that you can share on LinkedIn and your CV. This course is also Course 1 of the MLOps Specialization, which is being developed with future university accreditation partnerships in mind. Completing the full specialization will give you a credential that goes beyond a typical online course certificate.
MLOps Fundamentals is Course 1 — it gives you the complete field overview and mental model. The MLOps Specialization includes deeper follow-on courses: Machine Learning Model Lifecycle, CI/CD for MLOps, Observability, Security, and FinOps. You can take this course alone, or as the entry point to the full specialization. The specialization roadmap is shown in detail on this page.
Yes. We offer team licenses for 5–20 engineers with a manager progress dashboard, kickoff session, and dedicated support. Contact us at [email protected] for team pricing.
An ISA program is currently in development for qualifying students – meaning you pay nothing upfront and only repay a small portion of your salary increase after landing an MLOps-related role. Contact us if you’re interested in joining the waiting list.
The course is deliberately tool-agnostic at the concept level — you’ll learn what each category of tooling does and why, using tools like MLflow, Docker, Kubernetes, and GitHub Actions as concrete illustrations. Deep, hands-on implementation of specific tools is covered in the follow-on specialization courses (CI/CD for MLOps, Model Lifecycle). This approach means you’ll understand any tool you encounter, not just one.
Yes – the introduction video is available free on this page and on the Nubes Academy YouTube channel. It gives you a clear sense of teaching style, depth, and pacing before you purchase. If you have specific questions about fit, email us directly and we’ll help you decide.

Still have questions? Email us directly — we reply within 1-2 business day.

It’s not just skills. It’s your next chapter.

Enroll Now

Stay ahead

New courses.
Better price.
Subscribers first.

  • Subscriber-only discount on every new course in the MLOps Specialization – before public launch price applies
  • Early access to new courses when they go live.
  • MLOps job market insights – what skills employers are actually hiring for, based on real job description data
  • Practical guides & free content – no noise, only what matters for engineers moving into MLOps

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