80% of ML models never leave the lab. MLOps is the engineering discipline that closes that gap — building the training infrastructure, deployment pipelines, feature stores, and monitoring systems that turn experiments into production systems.
The problem MLOps solves
The skills gap isn't in model architecture or algorithm selection — it's in everything that happens after the model trains. This bootcamp is entirely about that second half.
Model runs in a Jupyter notebook, only the data scientist who wrote it knows how to run it again
Training is not reproducible — different hardware, different results, no experiment log
Deployment is manual: exporting a pickle file, emailing it to a DevOps engineer who copies it to a server
No monitoring — nobody knows when the model starts returning wrong predictions
Retraining is a manual scramble when someone notices the model stopped working months ago
No version control for models or features — impossible to roll back when a deployment breaks production
Every experiment is tracked with metadata — any run is reproducible by anyone on the team
Automated training pipelines run on schedule or on data-drift trigger, no manual intervention
Models deploy via a CI/CD pipeline with canary, blue-green, or shadow strategies and automatic rollback
Data drift and model performance are monitored 24/7 with alerts before users notice degradation
Retraining triggers automatically when drift exceeds thresholds — models stay current
Full model registry with lineage, versioning, and approval gates — production deployments are auditable
Study map
Click any lesson to see all 10 chapter titles inside it. The full schema was reviewed and approved before any content was written — what you see is exactly what ships.
What you'll master
Every tool, platform, and concept taught in this bootcamp is tied directly to a specific stage of the ML lifecycle diagram you saw above.
How each chapter works
Every chapter follows a consistent structure so you build a study rhythm once and apply it across the whole bootcamp.
What it is, how it works, and where it fits in the MLOps lifecycle — with real-world context, not abstract theory.
How teams actually configure and deploy this — concrete AWS commands, SDKs, YAML, and architecture patterns.
When to use this over alternatives — MLflow vs SageMaker Experiments, Airflow vs Kubeflow vs Step Functions, etc.
What breaks in production, how to diagnose it, and how to design around known failure patterns.
How this chapter's concept fits into a full ML platform serving dozens of models in multiple environments.
Scenario-based task using realistic AWS, Kubernetes, and ML tooling environments. Based directly on sections A–E.
MCQs with full answer explanations. Designed to simulate real certification-style and interview questions.
Where this leads
MLOps engineers are among the most in-demand engineering profiles in the EU in 2026 — the global market is growing at 22%+ CAGR according to Optiveum, driven by enterprises moving past the AI experiment phase into production deployment. Figures below are gross annual base salary. Select a country.
Entry into MLOps typically requires either a DevOps background adding ML pipeline skills, or an ML background adding infrastructure skills. Glassdoor Germany data (25th–75th percentile: €62,200–€85,250, n=16). Berlin, Munich, and Hamburg lead in junior MLOps openings, driven by automotive AI, fintech, and logistics ML platforms.
Owns full ML platform components — training pipelines, feature stores, model registries, and monitoring. TechPays/Levels.fyi reports €83,517 median MLOps in Germany. ERI SalaryExpert puts ML Engineer average at €100,264. Bluecoders (EU-wide) confirms mid-level €70–95K. Strong demand from German manufacturing, insurance, and healthcare AI platforms.
Senior MLOps engineers architect the full ML platform strategy and own GPU infrastructure, multi-cloud deployments, and FinOps for ML. Bluecoders confirms €95–130K for senior/lead (EU-wide). LLMOps experience commands premiums at this level — roles at frontier AI-first companies can exceed €130K. Equity compensation common at scale-ups.
Sources: Glassdoor Germany MLOps (Dec 2025, n=16, €62.2K–€115K range), TechPays/Levels.fyi Germany (€83,517 MLOps median), ERI SalaryExpert Germany ML Engineer (€100,264 avg), Bluecoders EU MLOps report (June 2026, Junior €50–70K, Mid €70–95K, Senior €95–130K).
Amsterdam and Eindhoven lead NL demand for MLOps profiles. AI-first fintechs, logistics platforms (Booking, bol.com), and ASML's ML teams are active hirers. ERI reports average NL ML Engineer at €89,633. DigitalDefynd puts NL ML Engineers at ~€70K average, implying entry-level sits comfortably below this.
Mid-level MLOps commands €82–108K in the Netherlands, consistent with ERI's €89,633 average for ML Engineers. Amsterdam typically pays 15–20% above the national average for the same role. Strong demand from Dutch banks adding AI fraud detection, e-commerce ML ranking models, and IoT manufacturing platforms.
Senior NL MLOps roles at major tech companies (Booking, Adyen, Philips AI) and US tech EU offices in Amsterdam reach the upper end of this range. NL's 30% ruling makes it significantly attractive for senior engineers relocating from outside the EU — effective tax burden drops substantially under this scheme.
Sources: ERI SalaryExpert Netherlands ML Engineer (€89,633 average), DigitalDefynd EU AI salaries (NL ML Engineer ~€70K average, 2026), Bluecoders EU MLOps report (June 2026), Optiveum ML Engineer salaries EU (March 2026).
Brussels, Antwerp, and Ghent are the primary Belgian markets for ML engineering profiles. EU institution proximity in Brussels creates demand for MLOps engineers on public-sector AI transparency and compliance projects. Belgium's ~50% income tax burden means net take-home is significantly lower than equivalent German or Dutch roles in gross terms.
Many Belgian ML engineers operate as B2B contractors to manage the tax burden more effectively. Strong demand from Belgian banks (KBC, Belfius), telcos, and Pharma (UCB, Johnson & Johnson EU) deploying production ML. The EU AI Act's compliance requirements are creating specialist MLOps demand in regulated industries specifically.
Senior Belgian packages commonly include company cars, meal vouchers, group insurance, and phone/internet allowances that offset the income tax burden — total compensation packages are more competitive than the gross figure alone suggests. EU institutions in Brussels also offer unique benefits packages with different tax treatment for international staff.
Sources: ERI SalaryExpert Belgium (2026), Bluecoders EU MLOps report (June 2026), Optiveum ML Engineer EU guide (March 2026). Belgium-specific MLOps data is limited — figures interpolated from EU-wide benchmarks with Belgian market adjustment and tax context applied.
Is this for you
MLOps is where DevOps skills meet ML production needs. This bootcamp bridges the two — but it assumes you're already competent in at least one side of that equation.
You have a DevOps/cloud infrastructure background and want to move into the ML platform space
You're an ML engineer or data scientist who keeps hitting the wall when trying to get models into production
You work with AWS regularly and want to go deep on SageMaker, EKS, and the AWS ML service stack
You want to build the platform that lets ML engineers deploy models without needing to call a DevOps engineer
You want to understand the tools — MLflow, Kubeflow, Airflow, Feast, KServe — not just read their marketing pages
You want material that's still relevant when you're six months into an MLOps role and hitting real production problems
You have no prior experience with cloud infrastructure, containers, or CI/CD — start with the DevOps Beginner Bootcamp first
You want to learn how to train ML models or improve model accuracy — this is about operationalising models, not building them
You're looking for Python or ML framework tutorials (TensorFlow, PyTorch) — this assumes you already work with code and focuses on the operational layer
You need live instruction or a fixed cohort schedule — this is entirely self-paced written material
You want Azure ML or GCP Vertex AI content — this bootcamp is AWS-focused throughout
Full MLOps Fundamental Bootcamp — all 10 lessons, 100 chapters — unlocked immediately on purchase.