AI and machine learning · Free online course

MLOps Model Deployment and Monitoring

The half of machine learning that starts after the notebook: a reproducible environment and a baseline model; experiment tracking, model management and the registry with MLflow; turning a notebook into a pipeline that can be re-run and backfilled; batch, web-service and streaming deployment in Docker; monitoring data quality, drift and model quality with Evidently, PostgreSQL and Grafana; and the tests, linting, make targets and GitHub Actions pipeline that let a release happen without anybody copying a file to a server.

15 modules
12h 5m of video
Intermediate
English
₹0, free

MLOps Model Deployment and Monitoring course cover

₹0Free for good

Worth ₹6,999 by length and depth against classroom training. It has always been free; that is not a former price.

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Overview

About MLOps Model Deployment and Monitoring

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 12h 5m of video lessons by DataTalksClub, Abhishek.Veeramalla, with written notes and worked problems, a practical project with a document pack and a 15-question final assessment (pass mark 60%). Learning is free with an account; an optional certificate with a public verification code is issued when you pass. Last updated 27 September 2026.

Who it is for: Engineers, developers and students applying AI to real work

This course includes

12h 5m of video lessons
15 modules with written notes and diagrams
One final assessment: 15 questions, pass mark 60%
Ask-an-engineer support from practising engineers
Lifetime access, the course does not expire
Optional verifiable certificate when you finish

Step 1 of 3
Learn the 15 modules
12h 5m of video lessons, written notes and a practice task per module, at your own pace.
Step 2 of 3
Pass the final assessment
15 questions drawn from every module, pass mark 60%, 3 attempts.
Step 3 of 3
Take the certificate, if you want it
Optional, paid once, only if you choose it. The course stays free either way.
See the certificate and its price

A look inside

MLOps Model Deployment and Monitoring: Syllabus at a glance — Syllabus at a glance

MLOps Model Deployment and Monitoring: What you will be able to do — What you will be able to do

MLOps Model Deployment and Monitoring: Tools and credits — Tools and credits

From the lessons

Why MLOps exists, and the maturity model
by DataTalksClub
The baseline model you will put into production
by DataTalksClub
Model management and the model registry
by DataTalksClub
From notebook to a pipeline that can be re-run
by DataTalksClub
Serving from the registry, batch scoring and streaming
by DataTalksClub
Metrics, dashboards and the monitoring service
by DataTalksClub

Lesson frames belong to the creators named in the Credits and are shown from YouTube.

What you learn

What you will be able to do

Rebuild a training environment from a pinned requirements file; 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 it back; turn a notebook into a parameterised pipeline script; decide between batch, web service and streaming and build the one you chose as a Docker image; load a model from the registry inside the service; write a batch scoring job that saves inputs beside predictions; build a reference dataset and monitor data quality, input drift, prediction drift and model quality into PostgreSQL and Grafana; and defend the repository with pytest, integration tests, linting and a GitHub Actions pipeline that releases without a manual copy step.

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
Decide between batch, web service and streaming, then build the one you chose as a Docker image
Monitor data quality, input drift, prediction drift and model quality with Evidently, PostgreSQL and Grafana
Defend the repository with pytest, integration tests, linting and a GitHub Actions pipeline that releases without a manual copy

Syllabus

Course content, module by module

In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.

Modules: 15
Video lessons: 39
Of video: 12h 5m
Final questions: 15

01
Why MLOps exists, and the maturity model
3 lessons · 32m
02
The working environment for a production model
5 lessons · 1h 20m
03
The baseline model you will put into production
1 lesson · 41m
04
Experiment tracking with MLflow
3 lessons · 50m
05
Model management and the model registry
2 lessons · 52m
06
MLflow in practice, and where it stops
2 lessons · 54m
07
From notebook to a pipeline that can be re-run
2 lessons · 41m
08
Three ways to deploy, and a web service in Docker
2 lessons · 42m
09
Serving from the registry, batch scoring and streaming
3 lessons · 51m
10
What to monitor once the model is live
3 lessons · 42m
11
Metrics, dashboards and the monitoring service
4 lessons · 45m
12
Data quality checks, test suites and unit tests
3 lessons · 1h 2m
13
Integration tests and code quality
2 lessons · 1h 6m
14
Automating the release: make and CI/CD
4 lessons · 1h 6m
15
Final assessment
0 lessons · 0m
Final assessment · 15 questions, pass mark 60%

Show all 15 modules

Project

The course project

Optional practice that ends in the kind of job the course prepares you for, with the documents that go with it.

About 18 hours

Bearing-fault classifier as a monitored service for a 40-fan cement plant fleet

Take a trained scikit-learn bearing-fault classifier and deliver it the way a plant can run it: a pinned environment, the model registered in MLflow with a model card, a Flask service in Docker that scores 40 fans every ten minutes, a nightly batch job, Evidently drift monitoring into PostgreSQL and Grafana, pytest and a GitHub Actions pipeline, and a runbook with a rehearsed rollback. You deliver the repository, your own model register and test report, and the runbook. The sample pack shows what each one looks like when a contractor delivers it to a plant.

Sample document pack, 8 documents, filled in for the scenario

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
O&M manualOperation and Maintenance Manual: Fan Condition Service (Control-room section)
Change noticeDesign Change Notice: Reference Set Rebuilt and Image Retention Policy

Read inside the course and download as a workbook. The project is optional practice, marked when you submit it; the certificate needs only the modules and the final assessment.

Tools

Software, hardware and lessons

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.

Who it is for

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.

Video lessons by

DataTalksClub
Abhishek.Veeramalla

Independent creators, credited in full under Credits.

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.

Required

01
Python
Python Software Foundation
Free
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.
Show the 5 stepsHide the steps
Steps
1. 1.Open python.org/downloads and click the download button for your system.
2. 2.On Windows, run the Python install manager (or the classic 64-bit installer) you downloaded.
3. 3.If you use the classic installer, tick "Add python.exe to PATH" on the first screen, then click Install Now.
4. 4.Open a new Command Prompt or Terminal and run: python --version (on Windows you can also run: py --version).
5. 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.org
Alternatives
- Anaconda Distribution: Python with 600+ data science packages and Jupyter already included.
02
Git
Git project (git-scm.com)
Free
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.
Show the 5 stepsHide the steps
Steps
1. 1.Open git-scm.com/downloads and choose your system.
2. 2.On Windows, download the x64 Setup (or ARM64 Setup) and run it. The default choices are fine for most learners.
3. 3.Open a new terminal and run: git --version
4. 4.Set your name: git config --global user.name "Your Name"
5. 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
03
Docker Desktop
Docker, Inc.
Free for personal use, education and small businesses
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.
Show the 5 stepsHide the steps
Steps
1. 1.Open docker.com/products/docker-desktop and download the installer for your system.
2. 2.On Windows, install WSL first (wsl --install) if you do not have it.
3. 3.Run the installer and keep the WSL 2 option selected.
4. 4.Restart if asked, then start Docker Desktop and accept the subscription terms.
5. 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.com
Alternatives
- Docker Engine (Linux): Free, open source engine for Linux. Not covered by the Docker Desktop subscription terms.
04
MLflow
MLflow project (Linux Foundation, created by Databricks), a Python or npm package
Free
Runs on
Windows, macOS and Linux with Python 3.10 or later
Account
None needed
Free, open source under the Apache 2.0 licence.
Show the 4 stepsHide the steps
Steps
1. 1.Install Python 3.10 or later.
2. 2.Create and activate a virtual environment, then run: pip install mlflow
3. 3.Start the tracking server and UI with: mlflow server
4. 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.

05
Grafana
Grafana Labs
Free
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.
Show the 5 stepsHide the steps
Steps
1. 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. 2.Pick your platform and download the installer or package.
3. 3.Install and start the Grafana service.
4. 4.Open http://localhost:3000 and sign in with the default admin account (admin / admin), then set a new password.
5. 5.Add a data source, such as InfluxDB, and build your first dashboard.
- Change the default admin password straight away.
Official download pagegrafana.com
06
Visual Studio Code
Microsoft
Free
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.
Show the 4 stepsHide the steps
Steps
1. 1.Open code.visualstudio.com/download.
2. 2.Pick the installer for your system (on Windows, the User Installer is fine).
3. 3.Run the installer. On Windows, tick "Add to PATH" if it is offered.
4. 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.

What you walk away with

Your certificate for MLOps Model Deployment and Monitoring

Finish the course, pass the final, and this is the document with your name on it.

Sample EDWartens Certificate of Completion for MLOps Model Deployment and Monitoring — Sample. The issued certificate carries your name, admission number, a unique certificate number and its own QR code.

Verifiable by anyone
A unique certificate number and a public verification page. A recruiter checks it in ten seconds.
Adds to LinkedIn in one click
Issuer, credential ID and URL prefilled, with 5 matching skills to pin to your profile.
QR code on the certificate
Scans straight to the verification page, so a printed copy proves itself.
Names what you can do
Lists the topics covered, from experiment tracking, model management and the mlflow model registry to unit and integration testing, linting, make and a github actions release pipeline.
A permanent link
Put it on a CV, a portfolio or an application. The URL never changes.
Earned, not attended
Issued only after every module and a final assessment at 60%, with 3 attempts. That is why it stands up.

Learning is free. The certificate is optional.

Add it now and pay only when you have finished the course, or come back for it later. One-off, US$28.99, with a receipt.

Issued by EDWartens India (Wartens Automation Private Limited) as a Certificate of Completion for this self-paced course. It is not a vendor certification, a university award or a CPD-accredited activity, and it does not certify competence on live equipment. Delivered electronically; see the refund policy.

FAQ

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?

One model taken the whole way: tracked in MLflow, registered and promoted, served both as a Flask endpoint in Docker and as a batch scoring job, monitored for data quality and drift on a Grafana dashboard, and 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.

Is the MLOps Model Deployment and Monitoring course free in India, and what does the certificate cost?

Yes. Learning costs ₹0 in India: every module, the written notes, the practice tasks and the final assessment, with no card and no trial period. The only paid item is the optional EDWartens Certificate of Completion, ₹599 including GST for this intermediate course, paid in rupees through Razorpay, and only if you want it after passing the final assessment.

All course facts

Price: ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹6,999.
Who it is for: Engineers, developers and students applying AI to real work
Format: 15 self-paced modules, 12h 5m of video, written notes, a practice task per module and one final assessment.
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.
Brand: Vendor-neutral
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.
Certificate: Optional EDWartens Certificate of Completion, verifiable by code. Not a vendor credential.
Video lessons by: DataTalksClub, Abhishek.Veeramalla (independent creators, credited below)
Language: English
Last updated: 27 September 2026

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Credits

Who made the video lessons

The video lessons in this course were created by the people below, not by EDWartens. Every lesson streams from its creator's own YouTube channel; EDWartens neither hosts nor sells that footage, and the creators are not affiliated with EDWartens and do not endorse this course. What EDWartens wrote is the study plan, the notes, the practice tasks and the assessments.

DataTalksClubthe MLOps Zoomcamp lessons: environment and baseline model, MLflow tracking and the model registry, pipelines, Flask, Docker and batch deployment, monitoring with Evidently and Grafana, pytest and integration tests, linting, make and GitHub Actions
Abhishek.Veeramallathe plain-language explanation of MLOps against AIOps, and the CPU against GPU lesson in the environment module

If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.