MLOps Specialization
End-to-End Machine Learning Operations
The MLOps Specialization is a structured 8-course program covering the complete Machine Learning Operations discipline — from foundational concepts to production security and compliance. Designed for DevOps engineers, platform engineers, and software engineers who want to own the full ML production stack, not just one part of it.
- Master the complete MLOps lifecycle — model management, CI/CD automation, data pipelines, observability, and compliance
- Work with both classical ML systems and LLM/GenAI workloads throughout the specialization
- Hands-on labs in every course, capstone projects with instructor review, and a certificate for each course completed
- Earn the MLOps Specialization Certificate upon completing all 8 core courses — designed toward university accreditation
- Start now: 3 courses are already live. New courses release step by step. Your access to each course doesn’t expire while you wait.
The complete MLOps curriculum
Every skill an MLOps engineer needs to own production ML systems — end to end.
The MLOps discipline covers eight distinct engineering domains. The specialization maps one course to each domain, in the sequence you’d encounter them building and operating a real ML system. Each course can be taken alone. Together they form a complete, structured qualification.
ML System Foundations
The AI/ML landscape, the full MLOps lifecycle, the tooling ecosystem, and the career paths of the engineers who operate ML systems. Start here if you’re new to the field or need a structured overview.
Python for ML Operations
The Python skills MLOps engineers actually use — file I/O, serialization, logging, testing, NumPy, Pandas — explained in MLOps contexts, not as a generic programming course.
ML Algorithm Operational Knowledge
How each model family behaves in production — resource profiles, latency characteristics, drift patterns, and failure modes — for the engineer who runs them, not the researcher who builds them.
Model Lifecycle Management
Experiment tracking, model registry, promotion gates, serving patterns, drift monitoring, retraining triggers, and rollback runbooks — from first experiment to last incident.
CI/CD Automation for ML
Data CI, training CI, model packaging, registry promotion, deployment pipelines, and safe rollouts — replacing manual ML deployment rituals with automated, validated pipelines.
Data Engineering for ML
Ingestion, versioning, feature stores, validation gates, label management, and LLM training data — treating data with the same engineering rigour as code.
ML Observability & Monitoring
Metrics, logs, traces, drift detection, Grafana dashboards, alert routing, and LLM-specific observability — full visibility into what production ML systems are actually doing.
ML Security & Compliance
Access control, supply chain security, LLM guardrails, EU AI Act, GDPR, NIST AI RMF compliance, fairness checks, and incident response — keeping ML systems safe, auditable, and trusted.
Core curriculum
8-Course Core Specialization Path
The core path runs in numbered sequence — each course builds on the previous. You can take courses individually or work through the full path for the Specialization Certificate. Start with Course #1 now and continue as new courses release.
Optional · Senior / Lead level
Advanced Track
Two optional deep-dive courses for engineers who want to go further — beyond the core specialization into the advanced topics that senior and lead MLOps roles require. Neither is required to earn the Specialization Certificate.
Advanced Python patterns used in production ML platforms — decorators, dynamic imports and plugin architectures, advanced OOP, concurrency and parallelism (threading, multiprocessing, synchronized queues), production serialization (Parquet, HDF5), Cloud SDK and Kubernetes Python SDK integrations, and a complete module building ML APIs with Flask and FastAPI.
Operational knowledge of the advanced model families senior MLOps engineers encounter in production — Transformers and large NLP models (cold start, tokenization overhead, privacy-safe serving), time series forecasting (seasonal drift, temporal baseline windows), anomaly detection, recommender systems (feedback loops, popularity bias, cold start), and probabilistic/Bayesian models (uncertainty calibration, confidence monitoring). Includes the extended model selection framework.
Specialization credential
MLOps Specialization Certificate
Complete all 8 core courses and earn the MLOps Specialization Certificate — a comprehensive credential covering the full Machine Learning Operations discipline. Every course in the core path also awards its own individual Certificate of Completion, shareable on LinkedIn as you progress.
Nubes Academy is developing university accreditation partnerships for the full specialization. The intent is for this credential to qualify as a recognized professional certification — not just another collection of online course certificates. Engineers who build the full portfolio now are positioned to have their work recognized as the accreditation process matures.
Is this specialization right for you?
Built for engineers who want to own ML systems in production — not just one part of them
The specialization is designed for engineers who work with ML systems professionally — or are moving into roles where they will. You don’t need a data science background. You need engineering discipline and a willingness to own the full operational stack.
What you need before starting:
✓ Recommended baseline
✗ You do NOT need
How it works
Take courses now. Continue as they launch.
Step-by-step release
Courses release one by one in numbered order. 3 are available now. Courses #4 through #8 are in production and will launch as they complete quality review. You don’t have to wait — start with what’s available and continue as new courses launch.
Self-paced, no deadlines
Each course is self-paced with 2 years of access (extension available). There are no cohort schedules or enrollment windows. Work through a module per day or binge an entire course in a weekend — the material is available whenever you are.
A certificate for each course
Every course awards its own Certificate of Completion immediately on finishing. You don’t have to complete all 8 courses to share credentials — each certificate is independently shareable on LinkedIn and your CV. The Specialization Certificate is awarded when all 8 are complete.
Pay per course
Each course is purchased individually. There’s no subscription or upfront bundle required. Subscribe to the newsletter to receive subscriber pricing on each new course launch — waitlist members consistently pay less than the public launch price.
Team training available
Team licenses for 5–20 engineers are available for each course, with a manager progress dashboard and kickoff session. If you’re planning a full-specialization team rollout, contact [email protected] to discuss group pricing across the full curriculum.
Be first when courses launch
Join the newsletter to be notified the moment each new course is available. Subscribers receive pricing below the public launch price for each course they’re waiting on. Sign up once, get notified for every new release.
Got questions?
Frequently asked questions
Still have questions? Email us directly — we reply within 1–2 business days.
Stay ahead of the releases
New courses.
Subscriber price.
You hear first.
- Launch notifications for every new course — Courses #4 through #8 and Advanced ML Algorithms will each send a subscriber-only notification the moment they’re live, before any public announcement
- Subscriber pricing on every launch — a discount off the public launch price on each course, exclusive to subscribers, with no commitment required
- MLOps job market insights — what skills employers are screening for in MLOps roles at mid, senior, and lead level, updated as the market evolves
- Practical guides and free content — no noise, only content relevant to engineers building and operating production ML systems
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