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EDWartens UK

Free course · AI and machine learning

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.

See the modules

Free to learn. Verifiable certificate £19 when you pass the final exam (optional, VAT included).

Level
Intermediate
Modules
15
Video
17 hours
Price
Free

Outcome

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

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.

Syllabus

15 modules

Each module has video lessons, notes and a short quiz. The final exam closes the course.

  1. 1Why MLOps exists, and the maturity model3 lessons
  2. 2The working environment for a production model5 lessons
  3. 3The baseline model you will put into production1 lessonLab
  4. 4Experiment tracking with MLflow3 lessons
  5. 5Model management and the model registry2 lessons
  6. 6MLflow in practice, and where it stops2 lessons
  7. 7From notebook to a pipeline that can be re-run2 lessons
  8. 8Three ways to deploy, and a web service in Docker2 lessons
  9. 9Serving from the registry, batch scoring and streaming3 lessons
  10. 10What to monitor once the model is live3 lessons
  11. 11Metrics, dashboards and the monitoring service4 lessons
  12. 12Data quality checks, test suites and unit tests3 lessonsLab
  13. 13Integration tests and code quality2 lessons
  14. 14Automating the release: make and CI/CD4 lessons
  15. 15Final assessment0 lessons

How it works

How this course works

  1. Watch the video lessonsOne module at a time, at your own pace.
  2. Take the module quizA short quiz at the end of each module shows what stuck.
  3. Keep your notesWrite in My notes beside each module and ask the trainer if you are stuck.
  4. Practise in the labOptionalTry a task from the module in the EDWartens Lab, in your browser.
  5. Sit the final examPass it once you have been through the modules.
  6. Certificate, if you want oneOptionalPaid, and only after you pass. Anyone can check it online.

EDWartens Lab

Practise this in the EDWartens Lab

Train a model on plant data, move the decision threshold and weigh the cost of errors in the Lab's AI workbench.

Works alongside MLOps Model Deployment and Monitoring. The Lab is ai for industry practice in your browser: no install, no hardware, free to use. Sign in as a learner and it opens with your account.

Open the free lab See the Lab
AI for industry: Build AI models.

Levels to try with this course

Optional. Skip it whenever you like: it never blocks your progress or your certificate.

Why practise in a 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.

Certificate

An optional certificate when you pass

Learning is free. Pass the final exam and you can have a certificate for £19, including VAT, with a number employers can check online. It records that you completed a free online course; it is not a regulated qualification or a CPD certificate.

  • Your name, the course, its study time and your final exam score
  • A unique certificate number and a QR code to its verification page
  • Issued by Wartens Ltd; add it to your LinkedIn profile in one click
  • Pay now or after you pass: it is issued the moment you pass the final

How the certificate works

Sample certificate for MLOps Model Deployment and Monitoring

Credits

Who made the lessons

The video lessons belong to their creators. EDWartens wrote the study plan, notes, quizzes and the final exam.

  • DataTalksClub

    the 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.Veeramalla

    the plain-language explanation of MLOps against AIOps, and the CPU against GPU lesson in the environment module

Questions

About this course

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.

The Professional Module is five live days of Siemens PLC, HMI and SCADA with an instructor, then a resume session and a CPD certificate.

See the Professional Module