AI and machine learning · Free · ₹0
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.
Inside the course
From the lessons
Why MLOps exists, and the maturity model
DataTalksClub ⬛
The baseline model you will put into production
DataTalksClub ⬛
Model management and the model registry
DataTalksClub ⬛
From notebook to a pipeline that can be re-run
DataTalksClub ⬛
Serving from the registry, batch scoring and streaming
DataTalksClub ⬛
Metrics, dashboards and the monitoring service
DataTalksClub ⬛
Lesson frames belong to the creators named in the Credits below and are shown from YouTube.
What you will learn
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
Course content
15 modules · 39 lessons · 12h 5m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last, and a short quiz.
- 01Why MLOps exists, and the maturity model4 lessons58m
- 02The working environment for a production model4 lessons53m
- 03The baseline model you will put into production1 lesson41m
- 04Experiment tracking with MLflow3 lessons50m
- 05Model management and the model registry2 lessons52m
- 06MLflow in practice, and where it stops2 lessons54m
- 07From notebook to a pipeline that can be re-run2 lessons41m
- 08Three ways to deploy, and a web service in Docker2 lessons42m
- 09Serving from the registry, batch scoring and streaming3 lessons51m
- 10What to monitor once the model is live3 lessons42m
- 11Metrics, dashboards and the monitoring service4 lessons45m
- 12Data quality checks, test suites and unit tests3 lessons1h 2m
- 13Integration tests and code quality2 lessons1h 6m
- 14Automating the release: make and CI/CD4 lessons1h 6m
- 15Final assessment0 lessons0m
Requirements
- 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.
- 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.
MLOps Model Deployment and Monitoring at a glance
MLOps Model Deployment and Monitoring is a free, self-paced online ai and machine learning course from EDWartens India with 15 modules, 12h 5m of video lessons, written notes, practice tasks and assessments, and an optional verifiable certificate.
- Price
- ₹0, free for good. No trial, no card. Comparable classroom training of this length costs about ₹6,999.
- Format
- 15 self-paced modules, 12h 5m of video, written notes, a quiz per module and a 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
A shareable EDWartens certificate
Finish every module and pass the final assessment, and the optional EDWartens certificate is yours. It carries a unique verification code on a public page anyone can check, so it stands up when a recruiter looks it up. See it below.
- Add it to your LinkedIn profile in one click
- Link it from a CV or portfolio, the URL is permanent
- Publicly verifiable by code, not a PDF anyone can edit
- Issued by EDWartens India, the training centre itself
The course itself stays free whether or not you ever buy one.
Stuck? Ask a practising engineer
A free course usually means a comment section and hope. This one does not. Every module has an Ask-your-trainer panel that reaches the same engineers who teach our paid programme: people who commission panels for a living, not moderators.
- Questions answered in the context of the module you are on
- The same engineers who teach the AEP programme
- Career tools, CV help and public job listings included
- Your progress and notes stay in your account for good
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Reviews here are written only by learners who have finished every module of MLOps Model Deployment and Monitoring, and they are published exactly as written. Finish the course and yours will be the first.
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 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 do I get?
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?
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.
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.
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 70%, with three 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, ₹559, GST included, 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.
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.