AI and machine learning
MLOps Model Deployment and Monitoring
Take a model from notebook to production: MLflow tracking, Docker deployment, drift monitoring and an automated release pipeline.
MLOps Model Deployment and Monitoring is a free, self-paced online course from EDWartens for engineers, developers and students applying AI to real work. It has 15 modules and takes about 16.6 hours, with written notes, a practice task per module, a practical project and a final assessment of 15 questions (pass mark 60%). Learning costs nothing and needs only a free account. The optional EDWartens Certificate of Completion, verifiable by code, is a one-off US$28.99 for you; it is not a vendor credential. Updated 1 November 2026.
- Free
- Intermediate
- 16.6 hours
- Self-paced
- Certificate optional
- Lab practice in your browser
No card, no trial. The course is free for good. Certificate US$28.99, optional.
- Who it is for
- Engineers, developers and students applying AI to real work
- Jobs
- Roles that use this skill: AI engineer, machine learning engineer, data analyst and automation developer. No course guarantees a job.
- Tools
- Python 3, MLflow, scikit-learn, Docker and docker-compose, Flask and gunicorn, Evidently, PostgreSQL, Grafana, pytest, black, isort, pylint, Git and GitHub Actions. All free. Hardware: A laptop with 8 GB RAM and about 10 GB of free disk. No GPU. No cloud account for the local route; AWS appears only in the lessons marked optional.
- Updated
After this course you can
By the end you will be able to:
- Log parameters and metrics to MLflow and register a model
- Turn a notebook into a parameterised pipeline script
- Build a batch or web service deployment as a Docker image
- Monitor data quality and drift with Evidently and Grafana
- Automate releases with pytest and GitHub Actions
Who it is for, and what you need first
Who it is for
Engineers, developers and students applying AI to real work.
Level: Intermediate. Intermediate. Best after the Machine Learning with Python course; you need working Python, the Linux command line, Git and enough Docker to run a container.
What you need
Software: Python 3, MLflow, scikit-learn, Docker and docker-compose, Flask and gunicorn, Evidently, PostgreSQL, Grafana, pytest, black, isort, pylint, Git and GitHub Actions. All free..
Hardware: A laptop with 8 GB RAM and about 10 GB of free disk. No GPU. No cloud account for the local route; AWS appears only in the lessons marked optional.. What to download, and how.
How this course works
Watch the lessons
Short video lessons, one module at a time, at your own pace.
Read the notes
Open the Reading tab for the written notes and diagrams of the module.
Practise in the 3D labOptional
Try the module in the free 3D lab, in your browser.
Check yourself
Answer the questions to see what stuck before you move on.
Final assessment
Pass the final once you have been through the modules.
Certificate, if you want oneOptional
Optional, with a page anyone can use to verify it.
What you learn
15 modules in order, at your own pace. 16.6 hours of video lessons, with written notes and a practice task in each module.
- 1Why MLOps exists, and the maturity model44m
- 2The working environment for a production model37m
- 3The baseline model you will put into production, with lab practice: 41m
- 4Experiment tracking with MLflow50m
- 5Model management and the model registry52m
- 6MLflow in practice, and where it stops54m
- 7From notebook to a pipeline that can be re-run41m
- 8Three ways to deploy, and a web service in Docker42m
Show the other 7 modules
- 9Serving from the registry, batch scoring and streaming51m
- 10What to monitor once the model is live33m
- 11Metrics, dashboards and the monitoring service54m
- 12Data quality checks, test suites and unit tests, with lab practice: 1h 2m
- 13Integration tests and code quality1h 6m
- 14Automating the release: make and CI/CD1h 6m
- 15Final assessment
Software you need
What to download, where from, what it costs and how to install it. Every link goes to the maker's own site, never a mirror.
scikit-learn, Flask, gunicorn, Evidently, pytest and the linters install with pip inside the course project.
Show what to download (6 tools)
Required
- 01Free
Python
Python Software Foundation
- Runs on
- Windows (not Windows 7 or earlier), macOS and Linux. Windows builds for x64, 32-bit and Arm64.
- Account
- None needed
Python is free, open source software. You can use it for learning and for commercial work at no cost.
Steps
- 1.Open python.org/downloads and click the download button for your system.
- 2.On Windows, run the Python install manager (or the classic 64-bit installer) you downloaded.
- 3.If you use the classic installer, tick "Add python.exe to PATH" on the first screen, then click Install Now.
- 4.Open a new Command Prompt or Terminal and run: python --version (on Windows you can also run: py --version).
- 5.Install packages with pip, for example: python -m pip install requests
- pip comes with Python. Run it as python -m pip so it always matches the Python you are using.
- Python.org now recommends the Python install manager on Windows. If it offers to add its folder to PATH, say yes so the python command works everywhere.
- Make a virtual environment for each project: python -m venv .venv
Official download pagepython.orgAlternatives
- Anaconda Distribution: Python with 600+ data science packages and Jupyter already included.
- 02Free
Git
Git project (git-scm.com)
- Runs on
- Windows (x64 and ARM64), macOS, Linux
- Account
- None needed
Git is free and open source software. Use it for any purpose at no cost.
Steps
- 1.Open git-scm.com/downloads and choose your system.
- 2.On Windows, download the x64 Setup (or ARM64 Setup) and run it. The default choices are fine for most learners.
- 3.Open a new terminal and run: git --version
- 4.Set your name: git config --global user.name "Your Name"
- 5.Set your email: git config --global user.email "you@example.com"
- On Windows you can also install with: winget install --id Git.Git -e --source winget
- A portable version (no installer) is offered for PCs where you cannot install software.
Official download pagegit-scm.com - 03Free for personal use, education and small businesses
Docker Desktop
Docker, Inc.
- Runs on
- Windows 10 64-bit version 22H2 (build 19045) or Windows 11 64-bit version 23H2 (build 22631) or later, with WSL 2 and hardware virtualisation on, 8 GB RAM. Also macOS and Linux.
- Account
- None needed
Free for personal use, education, non-commercial open source projects, and businesses with fewer than 250 employees and less than 10 million US dollars in annual revenue. Larger businesses need a paid subscription. Docker Engine on Linux is open source and is not covered by these terms.
Steps
- 1.Open docker.com/products/docker-desktop and download the installer for your system.
- 2.On Windows, install WSL first (wsl --install) if you do not have it.
- 3.Run the installer and keep the WSL 2 option selected.
- 4.Restart if asked, then start Docker Desktop and accept the subscription terms.
- 5.Open a terminal and run: docker run hello-world
- Turn on virtualisation in BIOS or UEFI if Docker says it is not available.
- On Linux servers, install Docker Engine instead of Docker Desktop.
Official download pagedocker.comAlternatives
- Docker Engine (Linux): Free, open source engine for Linux. Not covered by the Docker Desktop subscription terms.
- 04Free
MLflow
MLflow project (Linux Foundation, created by Databricks), a Python or npm package
- Runs on
- Windows, macOS and Linux with Python 3.10 or later
- Account
- None needed
Free, open source under the Apache 2.0 licence.
Steps
- 1.Install Python 3.10 or later.
- 2.Create and activate a virtual environment, then run: pip install mlflow
- 3.Start the tracking server and UI with: mlflow server
- 4.Open http://localhost:5000 to see your experiments and runs.
- In your training script, point MLflow at the server with mlflow.set_tracking_uri("http://localhost:5000").
Official download pagemlflow.org
Optional
Useful, not needed to finish the course.
- 05Free
Grafana
Grafana Labs
- Runs on
- Windows, macOS, Linux (Debian/Ubuntu, RHEL/Fedora, SUSE, ARM64) and Docker
- Account
- None needed
Grafana OSS is free and open source under AGPLv3. The Grafana Enterprise download is also free to use without a licence key and is functionally identical until you buy an Enterprise licence.
Steps
- 1.Open grafana.com/grafana/download and choose the edition (Enterprise is recommended by Grafana and is free to use; OSS is fine too).
- 2.Pick your platform and download the installer or package.
- 3.Install and start the Grafana service.
- 4.Open http://localhost:3000 and sign in with the default admin account (admin / admin), then set a new password.
- 5.Add a data source, such as InfluxDB, and build your first dashboard.
- Change the default admin password straight away.
Official download pagegrafana.com - 06Free
Visual Studio Code
Microsoft
- Runs on
- Windows 64-bit (supported Windows client versions), macOS (latest and two previous releases), Linux (Ubuntu 20.04, Debian 10, RHEL 8, Fedora 36 or later)
- Account
- None needed
- Size
- Less than 200 MB download, under 500 MB installed
Free to download and use. Extensions from the Marketplace each have their own licence.
Steps
- 1.Open code.visualstudio.com/download.
- 2.Pick the installer for your system (on Windows, the User Installer is fine).
- 3.Run the installer. On Windows, tick "Add to PATH" if it is offered.
- 4.Open VS Code and install the extensions your course uses, for example the Python extension.
- Hardware needs are small: a 1.6 GHz processor and 1 GB of RAM.
- On Windows, the "Open with Code" options in the installer let you open folders from File Explorer.
Official download pagecode.visualstudio.com
Checked against each maker's own page on 27 September 2026. Trial lengths and editions change; the maker's page is the final word.
Practise in the 3D lab
Practise two habits that keep a model honest in production: a clean split for the baseline, and data checks that catch a bad table before the model sees it.
- Train and evaluate without leakageAI Workbench, about 25 minOpen in the lab (opens in a new tab): Train and evaluate without leakage
- Data health checkAI Workbench, about 20 minOpen in the lab (opens in a new tab): Data health check
Optional. Skip it whenever you like: it never blocks your progress or your certificate.
Course project, about 18 hours
Fault classifier as a monitored service for a 40-fan cement plant fleet
Take a fault classifier for forty plant fan motors and deliver it the way a plant can run it. Track and register the model, serve it from a Docker image and score a day in batch. Monitor drift on a Grafana board and release through a pipeline. You submit the repository, your model register and test report, and a runbook a night-shift engineer can roll back from.
- 8documents in the pack
- 10steps to follow
- 5things to hand in
- 18 hof work, at your pace
- URSUser Requirements: Fan Bearing-Fault Prediction Service
- SDSSoftware Design Specification: Fan Classifier Service
- RegisterModel Register and Model Card: fan_bearing_clf
- ProcedureRunbook: Deploy, Roll Back, Monitor and Retrain the Fan Classifier
- Test reportTest Report: Contract, Load, Drift, Rollback and Retraining Trigger
- Risk registerRisk Register: Fan Classifier Service
The project is optional practice. The certificate needs only the modules and the final assessment.
After the course, your next step
Show what you can do, then keep going in the same field.
A certificate you can verify
Finish the modules and pass the final assessment (15 questions, pass mark 60%) and you can add a certificate with a public verification page. One-off, US$28.99 for you. It is not a vendor credential.
Want a free certificate? Do the matching Free internship: 4 weeks, nothing to buy, with a free certificate of completion.
AI and Machine Learning
Eight short weeks of machine learning from data to a deployed model: notes, diagrams, a few videos and worked exercises in free notebooks. Finish weeks 1 to 4 and the final, then claim a free Internship Completion Certificate with your real dates and study hours, and a QR code anyone can scan to verify it. Training, not employment.
See the internshipMore free courses
Roles that use this skill
- AI engineer
- Machine learning engineer
- Data analyst
- Automation developer
These are the kinds of roles where the skill is used. We make no promise of a job or an interview.
Plain notes
- Free: the whole course, the notes, the practice tasks, the final assessment and support from practising engineers.
- Paid, and optional: the course certificate, and the Engineer-verified internship (the Free internship costs nothing).
- No job promise: the course teaches a skill. It does not place you in a job.
- Lessons by: DataTalksClub, Abhishek.Veeramalla. Independent creators; the lessons stream from their own channels. EDWartens wrote the study plan, notes, practice tasks and assessments.
DataTalksClub · Abhishek.Veeramalla. If you are one of these creators and want a lesson removed or credited differently, write to info@edwartens.com.
Practise what you learn in the free 3D lab
Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.
- Practise safely, so a mistake costs nothing.
- The lab checks your work like a site test, so you know when it is right.
- Free, in your browser, with nothing to install.
Why learn with EDWartens
What you get with this course, and what comes after it.
- Free from the first lesson to the final assessment.
- Practice in a free 3D lab in your browser, with nothing to install.
- Video lessons with written notes and a project.
- An optional certificate that anyone can verify online.
- Free internships to keep practising afterwards.
Common questions
What do I need before this course?
Working Python, the Linux command line and Git, plus a model you have trained at least once. The Machine Learning with Python course covers that. You do not need prior Docker experience beyond running a container, because the deployment modules build the image step by step.
Do I need a cloud account or a paid subscription?
No. MLflow, Docker, Flask, Evidently, PostgreSQL, Grafana, pytest and GitHub Actions are all free, and the whole local route runs on a laptop. Two lessons demonstrate AWS Kinesis, Lambda and cloud deployment. They are marked optional, they are there for the design they teach, and nothing in the assessment depends on having an AWS account.
Do I need a GPU?
No. The model used through the course is a regression on tabular data that trains in under a minute on a CPU. There is a lesson on when a GPU is worth paying for, which for this kind of work is rarely.
Is this the same as the AWS or Google machine learning engineer certification?
No, and it is not preparation material sold by those vendors either. Those are paid, proctored vendor examinations with their own syllabus. This course teaches the practices those examinations test, using open-source tools, and it ends with an EDWartens Certificate of Completion. If you want the vendor badge you still have to sit and pay for the vendor's exam.
What will I have worked through by the end?
You work one model the whole way through. It is tracked in MLflow, registered and promoted. It is served both as a Flask endpoint in Docker and as a batch scoring job. It is monitored for data quality and drift on a Grafana dashboard. It is released by a GitHub Actions pipeline that runs the linter and the tests first.
Is the MLOps Model Deployment and Monitoring course really free?
Yes. Every module, practice task and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.
What certificate does the MLOps Model Deployment and Monitoring course give?
An EDWartens Certificate of Completion, issued when you have finished every module and passed the final assessment, with a verification code anyone can check. It is not a vendor credential and is never described as one.
Who made the video lessons in the MLOps Model Deployment and Monitoring course?
The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks and the assessments.
Do I have to use the lab?
No. The lab is optional practice. You can skip it whenever you like, and it never blocks your progress, the final assessment or your certificate.
Is the lab free?
Yes. Every lab level for this course opens in your browser, free, with nothing to install.
What topics does the MLOps Model Deployment and Monitoring course cover, module by module?
The MLOps Model Deployment and Monitoring course has 15 modules. It opens with "Why MLOps exists, and the maturity model", goes through "The working environment for a production model", "MLflow in practice, and where it stops", "What to monitor once the model is live" and "Automating the release: make and CI/CD" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.
What can I do after the MLOps Model Deployment and Monitoring course?
After the MLOps Model Deployment and Monitoring course: Log parameters, metrics and artefacts to MLflow and choose a model on the record rather than on memory; Register a model version, promote it, and roll back to the one it replaced; Turn a training notebook into a parameterised script a scheduler can re-run and backfill. These are the practical outcomes the course is written around, and the final assessment of 15 questions checks them.
What software and hardware do I need for the MLOps Model Deployment and Monitoring course, and does it cost anything?
For the MLOps Model Deployment and Monitoring course, software: Python 3, MLflow, scikit-learn, Docker and docker-compose, Flask and gunicorn, Evidently, PostgreSQL, Grafana, pytest, black, isort, pylint, Git and GitHub Actions. All free. Hardware: A laptop with 8 GB RAM and about 10 GB of free disk. No GPU. No cloud account for the local route; AWS appears only in the lessons marked optional. The course itself is free.
Is the MLOps Model Deployment and Monitoring course suitable for me, and what level is it?
MLOps Model Deployment and Monitoring is rated intermediate. Intermediate. Best after the Machine Learning with Python course; you need working Python, the Linux command line, Git and enough Docker to run a container. It is written for engineers, developers and students applying AI to real work, and takes about 16.6 hours at your own pace.
Which jobs use the skills from the MLOps Model Deployment and Monitoring course?
Skills from the MLOps Model Deployment and Monitoring course (for example: log parameters, metrics and artefacts to MLflow and choose a model on the record rather than on memory) are used in roles such as AI engineer, machine learning engineer, data analyst and automation developer, and in machine learning engineer work. The course gives you practical evidence of the skill; it does not promise a job, and employers decide on experience and interview.
Start free today
MLOps Model Deployment and Monitoring, at your own pace. Add the certificate only if you want one.