LAB · REPRODUCE
Run it again, get the same model
Take a notebook-trained model and make the run reproducible end to end.
> Machine Learning · DevOpsSchool Training
MLOps Training in Netherlands
Live online batches timed for CET (UTC+1), or corporate onsite across Netherlands — every session a live demo in a real lab, not slides.
★ 4.8 / 5 from 2,300+ ratings18,000+ engineers certifiedTrained teams at JPMorgan Chase, Verizon, Nokia, World Bank
Next batch—
TimezoneCET (UTC+1)
Modules12
Hands-on labs3
Engineers we've trained work atJPMorgan Chase · Bank of America · Wells Fargo · Verizon · Nokia · World Bank · GE Healthcare · VMware · Oracle · Qualcomm · Mercedes-Benz · Airbus · Datadog · Splunk · Deloitte · Infosys · Wipro · Capgemini
# upcoming batches
When the next MLOps batch runs in Netherlands
Times shown in CET (UTC+1). New cohorts start on the 1st of every month.
| Starts | Days & time | Timezone | Mode | Duration | Seats | Enrol |
|---|---|---|---|---|---|---|
| —Most popular | Weekend · Sat · Sun 10:00 AM – 1:00 PM | CET (UTC+1) | Live online · Corporate onsite | 5 weekends | — of 10 left | Reserve |
| — | Weekday · Mon · Wed · Fri 8:00 – 10:00 PM | CET (UTC+1) | Live online · Corporate onsite | 5 weeks | — of 10 left | Reserve |
Batches are capped at 10 learners by design. We do not run a classroom in Netherlands — sessions here are live online, or onsite at your office for corporate batches.Need a date that isn't listed? Talk to us about a private batch →
# how you attend
Four ways to take this training — three of them available here
Live online
Individuals, anywhere
Scheduled instructor-led batch. Every session is a live demo in a real lab, and every session is recorded.
Reserve my seatCorporate onsite
Teams of 8–30
Custom agenda built from your stack, delivered at your office or online, in your timezone. NDA-friendly, invoiced against a PO.
Request a quoteSelf-paced video
Self-starters
The full recorded curriculum plus a year of the entire LMS — 20+ courses and 50+ tools, not just this one.
Enroll now# private batches
Private corporate batch for your team
We start with a discovery call, map your stack to the modules below, and drop anything your team already runs in production. Delivery is at your office, online, or hybrid — in your timezone, scheduled around your release calendar.
- Custom agenda built from your stack, not a fixed syllabus
- Teams of 8–30; larger cohorts split into parallel batches
- Recordings, lab repos and per-attendee certificates
- Attendance and assessment report for your L&D team
- PO, tax invoice and NDA handled before delivery
Already know what you need?
Send us the tools, the team size and a rough window. You'll get an agenda and a quote back, not a sales call.
# netherlands
MLOps training in Netherlands
MLOps training in Netherlands is taken mostly by Data scientists whose models never reach production and ML engineers building deployment pipelines. Sessions run live online in CET (UTC+1), or onsite at your office — there is no Netherlands classroom. Teams here often pair it with our AIOps and Ansible training, and a corporate batch can cover more than one in a single engagement. The Netherlands is an unusual market because so much of the region's network traffic physically passes through it. AMS-IX and the Amsterdam data-centre cluster mean Dutch teams think about egress, peering and cross-region replication earlier than teams elsewhere in Europe, and questions that are academic in other cohorts are operational here. The second factor is regulatory. Dutch financial services, health and public sector organisations work to a stricter reading of data residency than most, and DORA has sharpened it further for anyone in scope. Engineers here need to evidence where data physically sits, who can reach it and what the audit trail looks like — so key custody, private connectivity and log retention get materially more attention in a Netherlands batch. Scheduling is the practical constraint. Our instructors teach from IST, four and a half hours ahead of CET in winter. The weekday cohort lands mid-afternoon CET, which most Dutch teams prefer; the weekend cohort starts early. Corporate batches are normally scheduled entirely inside CET business hours by arrangement.
Teams we've trained here
How we deliver in Netherlands
We do not run a classroom in Netherlands. Batches here are live online, or onsite at your office for corporate cohorts.
Our two classrooms are in Bengaluru and Hyderabad.
# the subject
What is MLOps?
MLOps applies delivery and operations practice to machine learning, where the artefact is a model rather than a binary and correctness decays over time. The pipeline covers data versioning, reproducible training, experiment tracking, a registry that holds candidate models with their metrics, deployment as a service or batch job, and monitoring for drift in both inputs and predictions. The difference from ordinary software is that a model can degrade without anything breaking. Nothing throws an error; the predictions simply get worse. Detecting that requires monitoring the data, not only the service.
Why it's in demand now
Most organisations that invested in machine learning have models that never reached production, or reached it once and were never updated. The scarce skill is the engineering around the model rather than the modelling: reproducibility, deployment, and knowing when a model needs retraining before a business owner notices.
# outcomes
What you'll be able to do
Make a training run reproducible from data version to hyperparameters
Track experiments so a result can be compared and recovered
Register, version and promote a model through stages
Serve a model with sensible latency and rollback
Monitor for data and prediction drift and trigger retraining
Explain to a stakeholder why a model needs replacing
# curriculum
12 modules. Live demos in a real lab, not slides.
Every module follows the same shape: 5 hrs · 2 assignments · 1 capstone.
01Overview of the certification programLive & Interactive
Welcome and Introduction, Overview of the certification program., Expectations and outcomes., Understanding MLOps, Definition and importance of MLOps., Key components of the MLOps lifecycle., Differences between traditional DevOps and MLOps., Machine Learning Basics
02Overview of machine learning conceptsLive & Interactive
Overview of machine learning concepts., Types of machine learning (supervised, unsupervised, reinforcement learning)., MLOps Lifecycle, Stages of the MLOps lifecycle: data collection, model training, deployment, monitoring, and maintenance., Importance of collaboration between data scientists and operations teams., Tools and Technologies, Overview of popular MLOps tools (e.g., MLflow, Kubeflow, TFX)., Setting up the environment for hands-on labs.
03Best practices for model developmentLive & Interactive
Data Management in MLOps, Data versioning and management techniques., Data pipelines and ETL processes., Tools for data management (e.g., DVC, Apache Airflow)., Model Development and Training, Best practices for model development., Experiment tracking and management., Introduction to automated ML (AutoML) tools.
04Using Docker and Kubernetes for model deploymentLive & Interactive
Model Deployment Strategies, Techniques for deploying machine learning models., Continuous integration and continuous deployment (CI/CD) for ML., Using Docker and Kubernetes for model deployment., Hands-on Lab: Model Deployment, Deploy a machine learning model using a selected tool (e.g., Flask, FastAPI)., Hands-on exercises to reinforce concepts., Model Monitoring and Maintenance
05Handling model drift and retraining strategiesLive & Interactive
Importance of model monitoring in production., Techniques for monitoring model performance., Handling model drift and retraining strategies., MLOps Governance and Compliance, Governance practices in MLOps., Regulatory compliance and ethical considerations in ML., Capstone Project, Group activity: Develop an end-to-end MLOps pipeline using learned concepts.
06Presentation of group projects and feedbackLive & Interactive
Presentation of group projects and feedback., Certification Exam, Review of key concepts., Administer the certification exam., Closing remarks and next steps.
07Understanding the Main Kubernetes Components (Optional)Live & Interactive
Welcome to MLOps Fundamentals, Why and When do we Need MLOps, Data Scientists’ Pain Points, The concept of DevOps in ML, Machine Learning Lifecycle, Understanding the Main Kubernetes Components (Optional), Introduction, Introduction to Containers
08Getting Started with GCP and QwiklabsLive & Interactive
Containers and Container Images, Lab Intro, Getting Started with GCP and Qwiklabs, Lab: Working with Cloud Build, Lab solution, Introduction to Kubernetes, Introduction to Google Kubernetes Engine, Compute Options Detail
09Services and ScalingLive & Interactive
Kubernetes Concepts, The Kubernetes Control Plane, Google Kubernetes Engine Concepts, Lab: Deploying Google Kubernetes Engine, Deployments, Ways to Create Deployments, Services and Scaling, Updating Deployments
10Jobs and CronJobsLive & Interactive
Rolling Updates, Blue-Green Deployments, Canary Deployments, Lab: Creating Google Kubernetes Engine Deployments, Jobs and CronJobs, Parallel Jobs, CronJobs, Part 2: Setting up the Tools
11Introduction to AI Platform PipelinesLive & Interactive
Introduction to AI Platform Pipelines, Overview, Concepts, When to use, Ecosystem, Lab: Running AI Platform Pipelines, Training, Tuning and Serving on AI Platform, System and concepts overview
12Build and push a training containerLive & Interactive
Create a reproducible dataset, Implement a tunable model, Build and push a training container, Train and tune a model, Serve and query a model, Lab: Using custom containers with AI Platform Training, Kubeflow Pipelines on AI Platform, System and concept overview
★Final certification examLive & Interactive
Open-book, scenario-based, taken in the LMS. Two free re-attempts and detailed feedback.
# hands-on
3 labs you build on your own cloud account
We don't hand out a sandbox that expires. You provision your own free-tier environment with our guidance, so the setup skill goes with you.
LAB · SERVE
Ship it and roll it back
Deploy a model behind an API, then roll back to the previous version under load.
LAB · DRIFT
Notice before the business does
Instrument input and prediction distributions and alert on meaningful drift.
# toolchain
The tools you'll actually touch
MLflowKubeflowDockerKubernetesDVCFeature storesModel registryPrometheusAirflowPython
Who this is for
- Data scientists whose models never reach production
- ML engineers building deployment pipelines
- DevOps engineers supporting ML workloads
- Platform teams building an ML platform
Pre-requisites
- Python
- Basic machine learning concepts
- Comfortable on a Linux shell
- Any experience with containers helps
How you're assessed
- Two assignments per module, reviewed by the instructor
- One capstone per module, pushed to your own GitHub
- 3 hours · online · open-book · scenario-based
- Two free re-attempts, with feedback
# pricing
Pick the level of support that fits
Self-paced video
₹833/mo
billed yearly ₹9,996
All recorded sessions, labs and the full LMS — at your own pace.
- Full 100+ hour recorded curriculum
- Hands-on capstones on your own cloud lab
- 1-year access — recordings, labs and updates
- 3-hr online open-book exam
- Certificate on completion
- Lifetime forum support
- Full LMS access — 20+ courses & 50+ tools
MOST POPULAR
Live & Interactive
₹34,999₹49,999SAVE 30%
works out to ₹2,917/moIllustrative monthly equivalent. The full amount is charged once at enrolment; we do not offer instalments.
5-week live cohort plus the complete LMS bundle.
- Everything in Self-paced
- 5-week cohort · 60+ hrs live instructor classes
- Weekly mentor office hours
- Cohort community and alumni access
- Capstone code review by the instructor
- Exam preparation and mock papers
- Lifetime forum support
- Full LMS access — 20+ courses & 50+ tools
1-on-1 Mentorship
₹99,999
works out to ₹8,333/moIllustrative monthly equivalent. The full amount is charged once at enrolment; we do not offer instalments.
A dedicated senior practitioner. Pace, schedule and labs tailored to you.
- Everything in Live & Interactive
- Private 1-on-1 instructor on your schedule
- Custom curriculum and labs for your stack
- Résumé and LinkedIn review
- Mock interview and salary negotiation prep
- Priority response from the instructor
- Lifetime forum support
- Full LMS access — 20+ courses & 50+ tools
+ 18% tax / VAT as applicable · Prices are charged in INR.
Cohort-cancellation refund
If we cancel or postpone a cohort and you decline the rescheduled session, you get a 100% refund within 15 days. Refund policy →
Terms & course material
Recordings, slides and lab repos are licensed to you for your own learning. Terms →
Your data stays with us
We don't share your details with third parties. Privacy →
# who teaches it
Taught by practitioners who run this in production
Rajesh Kumar — Principal DevOps Engineer & Architect
Twenty years across DevOps, SRE and security, in principal and architect roles at PayPay, SoftwareAG, ServiceNow, Intuit, Adobe and IBM. Has personally trained more than 10,000 engineers at JPMorgan Chase, Verizon, Nokia, the World Bank and dozens more. He teaches what he runs, not what he reads.
The wider faculty
# the credential
What you walk away with
- An industry-recognised digital certificate, verifiable on our site
- One capstone per module, public on your own GitHub
- A hard copy on request
- Lifetime access to the DevOpsSchool forum
Certificate of Completion
Your Name
MLOps Training in Netherlands
DS-MLOPS-XXXX-XXXX
# reviews
What learners say
4.8 / 5 from 2,300+ ratings.
Reviews for this program are being collected from public sources. Every review shown here links to the original.
# alternatives
How this compares
| What matters | YouTube + blogs | Generic online course | Local training institute | DevOpsSchool |
|---|---|---|---|---|
| Teaching method | Unstructured, no sequence | Pre-recorded slides | Slide-led classroom | Live demos in a real lab |
| Batch size | n/a | Unlimited | 30–60 | Capped at 10 |
| Lab environment | Yours to figure out | Shared sandbox that expires | Shared lab | You build your own — the skill goes with you |
| Per-tool structure | None | Video only | Varies | 5 hrs · 2 assignments · 1 capstone |
| Assessment | None | Quiz | Attendance | 3 hours · online · open-book · scenario-based |
| Certificate | None | Auto-issued | Attendance certificate | Industry-recognised, verifiable |
| Corporate invoicing | No | Card only | Sometimes | PO, tax invoice, NDA |
| Post-training support | None | Forum for 30 days | None | Lifetime forum support |
| Total cost | Free, and it costs you months | Low, low completion | High | One fee, LMS included |
# questions
Frequently asked questions
Do you run classroom batches in Netherlands?
No. We do not have a training centre in Netherlands, and we would rather say so than let you plan a commute. Batches here run live online, or onsite at your own office for corporate cohorts. Our two classrooms are in Bengaluru and Hyderabad.
What time do the sessions run in Netherlands?
Our instructors teach from India, so sessions are converted into CET (UTC+1) on the batch table above rather than quoted in IST. The weekday cohort lands more conveniently for Netherlands than the weekend one, and every session is recorded the same day. For a corporate batch we schedule entirely inside CET (UTC+1) working hours instead.
Is the MLOps content different for Netherlands?
The curriculum is the same everywhere — it is the same course, and pretending otherwise would be dishonest. What changes for Netherlands is delivery: the batch times are set for CET (UTC+1), fees are shown in EUR, and delivery is live online or onsite at your office rather than in a classroom. The examples our instructors reach for also tend to follow what Netherlands teams actually run.
Do you provide a lab environment for MLOps?
No, and that is deliberate. You build the labs on your own free-tier account, and we walk through the setup and the cost guardrails in week one. A sandbox that expires when the course ends teaches you nothing you keep; your own environment does.
What if I miss a session?
Every session is recorded and available in the LMS the same day, and you keep access for a year. You can also sit the missed session again with the next cohort at no extra cost.
What do I need to know before starting MLOps?
Python; Basic machine learning concepts; Comfortable on a Linux shell. If you are unsure whether you are ready, tell us what you work on now and we will give you a straight answer rather than a sales one.
Is there a certificate, and is it worth anything?
You get an industry-recognised DevOpsSchool certificate after the three-hour open-book exam. Most people find the capstone repositories carry more weight in an interview than the certificate itself, which is why the labs are built to be shown.
Can you run MLOps as a private batch for our team?
Yes. We build the agenda from your stack after a discovery call and drop anything your team already runs in production. Teams of 8 to 30 work best; larger groups split into parallel batches.
What is your refund policy?
If we cancel or postpone a cohort and you decline the rescheduled session, you get a full refund within 15 days. We do not offer a general money-back guarantee, and taxes and gateway fees are not refunded.
Do you offer instalments or EMI?
No. The fee is charged once at enrolment. Where we show a monthly figure it is arithmetic to help you size the cost against a budget, not a payment plan.
# elsewhere
MLOps training in other cities
# keep learning
Free MLOps resources
Everything below is open and free — no enrolment needed.
# in the room
Training gallery
# ready when you are
Reserve your MLOps Training in Netherlands seat — or ask first.
- No spam, and no sales script
- Curriculum in your inbox in 60 seconds
- A human reply within 4 hours