Typical roles
- You train models as a data scientist and want to get them into production.
- You build the infrastructure on which models are trained, deployed and monitored.
- You are responsible for a data platform that machine learning models run on.
tecnovy AI Suite
MLOps: operating machine learning systems automatically
A machine learning model can lose prediction quality in production even without a code change, because it depends on the data it learned from. When that data shifts in production, the model needs training again. In MLOps you build a pipeline that versions data and models, trains, tests and deploys models automatically, and detects drift. Afterwards you decide from measurements when a model is retrained.
Attendance certificate4.8/5 on ProvenExpert
What this module delivers.
A pipeline from a versioned dataset to a monitored model in production: you build it in tecnovy's MLOps if you train models or run their platform. Your team then trains reproducibly and releases models with a clear record.
How the training runs
- Method
- One end-to-end example pipeline, from data to monitoring
- Basis
- tecnovy curriculum for operating machine learning models
- Outcome
- tecnovy certificate of participation, no examination
Dates & booking
Choose a date that fits
No public dates are scheduled yet.
New dates are published regularly. Join the waitlist or plan an in-house course for your team.
Fit
Who this module is designed for
Prerequisites
No prerequisites
You can start right away. What you need: Python skills, machine learning fundamentals, working knowledge of Git and the command line.
Consider instead LLMOps For teams that run applications on ready-made language models instead of training their own: you version prompts and models, gate releases with evaluations and control costs.
Curriculum
MLOps in Detail
The five parts follow the path of a model: from how mature an ML process is, through data, experiments and automated pipelines, to deployment and monitoring in production. Every exercise runs on the same example pipeline.
01MLOps foundations
This part shows where an ML process stands today and who does what in it.
- Maturity levels: how far training, testing and delivery are automated
- Roles in the ML lifecycle
- Typical problems on the way into production
02Data and experiments
This part makes every model traceable to its data and its training run.
- Versioning data and models
- Experiment tracking: recording parameters, metrics and results for each training run
- Reproducible training runs
03Pipelines and automation
You automate training and checks so that every change takes the same path.
- Training pipelines
- CI/CD for ML: continuous integration and continuous delivery for code, data and models
- Automated tests for data and models
04Deployment
This part takes a tested model into production in a controlled way.
- Model registry: storing models centrally with version and status
- Releasing models
- Batch and real-time serving in containers
05Monitoring
In this part you check whether a model in production still fits the current data.
- Data drift: production data moves away from the training data
- Model drift: prediction quality declines
- Triggering and documenting retraining
Outcome
What you will be able to do afterwards
- 01
You determine which maturity level an ML process has reached and which role takes on which task in the lifecycle.
- 02
You version data and models so that every model can be traced to its training data.
- 03
You record experiments with their parameters and metrics and make training runs reproducible.
- 04
You build a training pipeline and connect it to CI/CD, which checks and delivers every change automatically.
- 05
You write automated tests for data and for models.
- 06
You register models in a model registry and release them in a controlled way.
- 07
You deploy models as batch jobs or as real-time services in containers.
- 08
You detect data and model drift, trigger retraining and document it.
tecnovy Teilnahmezertifikat
Confirms your attendance and the curriculum covered in the training. Issued by tecnovy, shareable on LinkedIn.
Open the Certificate Showroom tecnovy →
Offer scope
What is included
Included
- Project repository with an example pipelineYou practise every step on this repository, from data versioning to monitoring.
Why tecnovy
What you get on top with us
01
No Slideshow, Hands-On!
Promised: no PowerPoint marathon. We work in groups, tie theory to practice, and you get real project examples from our experienced trainers plus the exchange with like-minded people.
02
Certificate Showroom
Get your certificate of participation and, if you have one, add your exam certificate from E-Learning. Fully automated, beautifully designed. Just for you, only at tecnovy.
03
Flexible Date Change
If you are not able to attend the course, you can rebook your training free of charge up to one week before the start of the training.
04
Attend Twice, Pay Once
You are welcome to attend the training online again within a year as a refresher.
05
Learn from Experts
We always guarantee you the use of didactically and methodically first-class qualified trainers who draw their knowledge from training experience as well as professional practical and project experience.
FAQs
Frequently asked questions
01How does MLOps differ from DevOps?
MLOps applies the versioning, testing and automated delivery of DevOps to data and models, and adds regular retraining. Google Cloud's Architecture Center describes this continuous training as a property unique to ML systems (as of November 2026). In tecnovy's MLOps training you build both into one pipeline.
02What does drift mean for an ML model?
Data drift means the data in production moves away from the training data; model drift means the model's prediction quality declines. In tecnovy's MLOps training you monitor both, trigger retraining when needed and document it.
03What does a maturity level mean in MLOps?
A maturity level describes how far training, testing and delivery of models are automated. Google Cloud, for example, distinguishes three levels: manual process, ML pipeline automation and CI/CD pipeline automation (as of November 2026). In tecnovy's MLOps training you determine where your own process stands.
04Do I need MLOps if we only use large language models?
Only in part: tecnovy's MLOps covers operating models your team trains itself. If you run applications on ready-made language models, tecnovy's LLMOps training fits better, with prompt and model versioning, evaluation in the pipeline and cost control.
05Which tools does the MLOps training use?
tecnovy's MLOps training works with widely used open-source tools for versioning, experiment tracking, pipelines and monitoring. For an in-house training, tecnovy adapts the exercises to your own tool stack.
06What do the MLOps exercises need technically?
The exercises in tecnovy's MLOps training run in a project repository with an example pipeline, where you work with Python, Git and the command line. You deploy models there in containers, self-contained runtime environments that behave the same everywhere.
07Is there an examination or a certification at the end of MLOps?
No, MLOps is a tecnovy training with no examination and no certification. If you attend at least 80% of it, you receive the tecnovy certificate of participation.
What does your training at tecnovy look like?
MLOpsMLOps: operating machine learning systems automatically
No dates yet
We will let you know as soon as the next date is scheduled.