Corporate MLOps Training

Structured upskilling in ML lifecycle, CI/CD, observability, etc

Corporate Education Program · nubes.academy

87% of large enterprises are now deploying ML in production. Yet 59% of engineering leaders report a critical MLOps skills gap on the teams responsible for running those systems. The Nubes Academy Corporate Education Program gives your engineering team the structured MLOps upskilling that closes that gap — with manager visibility, instructor access, and credentials your team can use.

  • Structured MLOps learning path purpose-built for engineers who run production ML systems — not a generic DevOps catalogue with “ML” in the title
  • Manager progress dashboard showing completion, module scores, and time-to-complete per engineer
  • Instructor-led kickoff session and Q&A access — Alex works directly with your team, not just records videos
  • Team pricing for 5–20 engineers with volume discounts — significantly below the individual enrolment cost
  • Certificates of Completion and the full MLOps Specialization Certificate toward university accreditation
Request a Proposal Response within 1–2 business days · No commitment required
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5–20 EngineersPer team licence
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Manager DashboardCompletion & scores per learner
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Instructor AccessKickoff session + Q&A
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Verifiable CredentialsPer-course + Specialization cert
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50+ Hands-on LabsJob-description-validated curriculum

The business case

Your team is deploying ML. But production incidents, slow release cycles, and failed audits suggest the skills aren’t keeping up.

The gap between a team that can train models and a team that can operate them reliably in production is not a talent acquisition problem — it is a structured knowledge problem. The engineers are already on your payroll. The knowledge they need is specific, learnable, and transferable to your stack the day after each module.

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The average model takes 7–12 months to move from lab to production
According to Cisco’s AI Readiness Index, this delay stems from predictable, solvable bottlenecks: poor data quality practices, ad hoc deployment processes, missing reproducibility discipline, and no structured monitoring. These are not talent problems — they are knowledge problems that structured MLOps training directly addresses.
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Hiring a senior MLOps engineer costs more than training five
MLOps is the most acute skill shortage in AI engineering — demand 85/100, supply 35/100, average time-to-fill 6–7 months for ML positions. Waiting to hire is expensive and slow. Training your existing DevOps, platform, and software engineers into MLOps competence is faster, cheaper, and comes with institutional knowledge already embedded in the team.
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Only 21% of organisations report significant ROI from AI investments
DataCamp’s 2026 survey of 500+ enterprise leaders found that organisations pairing AI investment with structured workforce upskilling are nearly twice as likely to report strong AI ROI. The bottleneck is not the model — it’s the operational capability to deploy, monitor, and maintain it. That’s exactly what this programme builds.
Structured MLOps upskilling is not a training budget cost — it is an operational risk mitigation and AI ROI multiplier. The Nubes Academy Corporate Education Programme gives your team the MLOps knowledge stack your AI strategy depends on, delivered in the sequence that matches how production ML systems are actually built.

What your team gets

Everything in a single team licence

The corporate programme packages the full MLOps Specialization with team-specific features — manager visibility, instructor engagement, and credentials — into one annual licence. No per-module fees, no seat-by-seat negotiations.

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Full MLOps Specialization Access

Every engineer on the team licence gets access to all 8 core courses and both optional advanced courses as they release — the complete 10-course MLOps curriculum from field overview through security and compliance. Includes all video content, hands-on labs, quizzes, and capstone projects with instructor review.

8 core courses 2 advanced courses 50+ hands-on labs
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Manager Progress Dashboard

A dedicated management view showing each engineer’s progress — module completion percentages, quiz scores, lab completions, and time invested. Built for engineering managers and L&D teams who need to report on training progress without chasing individual engineers for status updates.

Per-learner progress Module scores Exportable reports
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Kickoff Session with the Instructor

A live kickoff call with Alex — covering your team’s specific technology stack, current MLOps maturity level, and the learning objectives that matter most for your organisation. The kickoff aligns the programme to your context rather than making your team fit a generic training path.

Live session Stack-specific guidance Recorded for the team
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Q&A and Capstone Review Access

Every engineer can ask questions at lesson level and receive answers from Alex in context. Capstone projects in every course are reviewed personally by Alex with written feedback — not auto-graded. Your engineers apply knowledge to real scenarios and receive expert review of their work.

Lesson-level Q&A Personal capstone review Written feedback
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Verifiable Credentials for Every Engineer

Each course awards a Certificate of Completion shareable on LinkedIn. Completing all 8 core courses earns the MLOps Specialization Certificate — a credential Nubes Academy is developing toward university accreditation. Your engineers build a verifiable professional portfolio, not just completion records in an LMS.

Per-course certificate Specialization certificate LinkedIn-shareable
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Flexible Curriculum Targeting

Not every team needs all 8 courses in sequence. We work with you to identify the highest-priority modules — a DevOps-to-MLOps transition team may start with Courses #4 and #5; a team deploying LLMs may prioritise the LLM-specific modules in Observability and Security. The kickoff session maps the curriculum to your team’s actual needs.

Priority mapping Full path or targeted Self-paced delivery

Team pricing

From €800 per seat / year

For teams of 5–20 engineers. Includes all 10 courses, manager dashboard, kickoff session, and Q&A access.
Compared to the individual enrolment cost of €250–300 per person per course, or €30,000+ to hire a single senior MLOps engineer.

Pricing is per seat per year, billed annually. Volume discounts available for teams above 10 engineers or multi-year commitments. Custom pricing for organisations with specific course selection or integration requirements.

Request a Custom Proposal →

Getting started

From enquiry to your team learning in under two weeks

The engagement process is designed to be fast and low-friction — no lengthy procurement cycles or LMS integrations required to get started.

1

Enquiry & brief call

Send us your team size, the courses you’re most interested in, and your timeline. We’ll respond within 1–2 business days and schedule a 30-minute discovery call to understand your team’s current MLOps maturity and what they need most urgently.

2

Proposal & licence setup

We send a tailored proposal with pricing, the recommended course sequence for your team, and the contract terms. Once approved, team seats are activated within 48 hours. No complex LMS integration — engineers access content directly through the nubes.academy platform with their existing accounts or new ones we set up.

3

Kickoff session with Alex

Within the first week, Alex runs a live kickoff session for your team. He explains the curriculum sequence, answers questions about how the material relates to your specific stack, and sets expectations for how labs connect to real engineering work. The session is recorded for engineers who can’t attend live.

4

Self-paced learning with manager visibility

Engineers work through the curriculum at their own pace — typically 3–5 hours per week. You have continuous visibility into their progress through the manager dashboard. Alex reviews capstone submissions and answers lesson-level questions throughout the licence period. Monthly check-in calls are available on request.

Is this right for your organisation?

Built for engineering organisations deploying or scaling ML in production

The programme is purpose-built for teams at a specific inflection point: you have engineers who understand DevOps, infrastructure, or software engineering, and you need them to own production ML systems with the same depth.

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Technology consultancies & professional services firms
Build MLOps delivery capability across your project teams
If your firm builds ML solutions for clients, your engineers need consistent, verifiable MLOps skills — not just familiarity with individual tools. The programme builds a shared MLOps knowledge base across your consulting teams, with credentials your clients will recognise. Particularly valuable for firms positioning toward EU AI Act compliance engagements.
→ Team licences mean every new project can draw from engineers who share the same lifecycle, CI/CD, and compliance vocabulary.
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Cloud service providers & system integrators
Extend your technical team’s ML platform capabilities
As enterprises demand end-to-end ML operations support, cloud providers and SIs need engineers who can go beyond “the model is deployed” to owning the full lifecycle — drift monitoring, retraining pipelines, compliance documentation, and incident response. The specialization covers every layer of the stack your clients now expect.
→ The MLOps Observability and Security & Compliance courses are particularly high-value for teams supporting enterprise ML deployments.
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AI-native startups & scale-ups
Build ML engineering rigour into your platform team early
Growth-stage AI companies face a specific challenge: the team that built the MVP doesn’t have the structured practices — CI/CD for models, proper registries, compliance documentation, security controls — that enterprise clients or regulators will demand. The programme installs that rigour across the team before it becomes a blocker to sales.
→ Early-stage investment in MLOps knowledge is significantly cheaper than retrofitting practices after the first failed enterprise audit.
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Financial services, healthcare & regulated industries
Build compliance-first ML operations from the start
The EU AI Act, GDPR, and sector-specific ML governance requirements are not going away. Regulated industries deploying ML need engineers who understand compliance controls, data protection patterns, model documentation requirements, and incident response obligations. Course #8 (Security & Compliance) specifically covers EU AI Act, GDPR, NIST AI RMF, and ISO frameworks in the context of ML systems.
→ The compliance, governance, and security courses are directly applicable to EU AI Act and GDPR compliance requirements for AI systems.
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Engineering & Tech Leaders
VPs of Engineering, CTOs, and technical directors who need measurable team skill growth and a visible return on the training budget.
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L&D and HR Teams
Learning and Development professionals who need verifiable credentials, measurable completion metrics, and curriculum aligned to job descriptions.
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Engineering Managers
Team leads who want their engineers to upskill consistently, track who’s progressing, and see the new knowledge applied to real production incidents.

Why not Pluralsight, Coursera, or A Cloud Guru?

Generic tech training platforms are broad. This programme is specific.

Broad platforms offer tens of thousands of courses with MLOps as one tag among many. They measure engagement. The Nubes Academy Corporate Programme measures competence — in the specific discipline your team actually needs.

Nubes Academy
Corporate Programme		 Pluralsight /
A Cloud Guru		 Coursera /
Udemy Business
MLOps depth 8-course end-to-end specialization Broad DevOps + cloud with MLOps modules University courses, not production-focused
Curriculum validation Validated against 205 MLOps job descriptions Broad industry standards Academic / vendor-led
Instructor access Direct — kickoff, Q&A, capstone review Community forums only Limited, typically peer forums
Capstone projects Every course, personally reviewed by instructor Auto-graded assessments Variable — peer or auto-graded
LLM/GenAI coverage Built into every relevant course Separate GenAI catalogue Separate courses
EU AI Act / compliance Dedicated course with hands-on labs General security content General compliance content
Accreditation pathway University partnerships in development Vendor certifications University degrees (expensive)
“Pluralsight can tell your team what Kubernetes is. Nubes Academy teaches them how to deploy an ML model to Kubernetes safely, monitor its predictions, detect drift, and roll back when the quality drops — which is what senior MLOps engineers actually spend their time doing.”

What your team will learn

The complete MLOps knowledge stack — in the order your engineers will apply it

The 8-course specialization maps to the eight domains of production ML engineering. Your team can follow the full path or start with the courses most relevant to their current gaps. View the full specialization page for complete curriculum details.

1
Available Now
MLOps Fundamentals
The MLOps field landscape, lifecycle stages, tooling ecosystem, and team roles. Foundation course for engineers new to the discipline.
View course →
2
Available Now
Python for MLOps
MLOps-focused Python — file I/O, serialization, logging, testing, NumPy, Pandas. Every topic taught through real MLOps pipeline contexts.
View course →
3
Available Now
ML Algorithms for MLOps
How ML model families behave operationally — resource profiles, latency, drift patterns, and failure modes. 9 hands-on labs. Capstone with instructor review.
View course →
4
Coming Soon
ML Model Lifecycle — Experiment tracking, registry, serving, drift, and rollback. 9 hands-on labs.
View course →
5
Coming Soon
CI/CD for MLOps — Data CI, training CI, model packaging, safe rollouts, golden pipeline template. 15 modules.
View course →
6
Coming Soon
Data for MLOps — Ingestion, versioning, feature stores, validation, labels, and LLM data management. 10 hands-on labs.
View course →
7
Coming Soon
MLOps Observability & Monitoring — Metrics, logs, traces, drift detection, Grafana dashboards, alert routing. 16 modules, 10 labs.
View course →
8
Coming Soon · Final Course
Security & Compliance for MLOps — EU AI Act, GDPR, NIST AI RMF, LLM guardrails, supply chain security, incident response. 15 modules, 14 labs.
View course →
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All 8 courses complete
MLOps Specialization Certificate — for every engineer on the team

View the full specialization page for complete curriculum details, module breakdowns, and individual course pages.

View Full Specialization →

Common questions

What procurement teams want to know

The minimum team size is 5 engineers. Pricing starts from €800 per seat per year, billed annually. This includes access to all 10 courses (as they release), the manager progress dashboard, the kickoff session, Q&A access, and capstone reviews. Volume discounts apply for teams of 10+ engineers or multi-year commitments. Contact us for a custom proposal and exact pricing for your team size.
The manager dashboard shows: completion percentage per course per engineer, module-level quiz scores, lab completion status, capstone submission status, and time invested. Access is provided to the account holder — typically the engineering manager or L&D contact. Individual learners do not see each other’s progress. Dashboard data can be exported for internal reporting. We can discuss specific reporting requirements during the discovery call.
Once the licence is active, engineers receive individual access credentials for the nubes.academy platform. There is no complex LMS integration or SSO setup required to start — engineers access content directly through the website. If your organisation requires SSO or SCIM provisioning for compliance or IT security reasons, contact us to discuss the implementation. Access is typically active within 48 hours of contract signing.
Yes — your team licence includes access to all courses in the specialization as they release, within the licence period. If Courses #4 through #8 launch during your annual licence, your engineers get access automatically at no additional cost. You will be notified when new courses become available. This means your team can start with Courses #1–#3 now and continue the path as the remaining courses launch.
The kickoff session specifically addresses this. For a team with solid DevOps and Python foundations, we typically recommend: Course #1 (MLOps Fundamentals) as a quick orientation even for experienced engineers — 2–3 hours for the field context — then Course #3 (ML Algorithms for MLOps) to understand operational model behavior, followed by Courses #4 and #5 (ML Model Lifecycle and CI/CD for MLOps) as the highest-value modules for DevOps engineers moving into MLOps. Course #2 (Python for MLOps) can be skipped by strong Python users or used for reference.
Engineers typically invest 3–5 hours per week to make meaningful progress. At that pace, each core course takes 3–6 weeks to complete (including lab time). The full 8-course path is a 6–12 month commitment at a reasonable pace. The programme is self-paced — there are no live deadlines or cohort schedules. Engineers can slow down during crunch periods and accelerate during quieter ones. The manager dashboard gives you visibility into whether the team is staying on track.
Each certificate is issued digitally with a unique credential ID and is shareable to LinkedIn. Nubes Academy is developing university accreditation partnerships for the full specialization — the intent is for the credential to qualify as a recognized professional certification. For EU AI Act compliance documentation purposes, the certificates demonstrate structured training in ML lifecycle, data governance, security controls, and compliance frameworks — all required knowledge areas under EU AI Act governance requirements. We can provide documentation of the curriculum mapping on request.
Targeted course selection is available. If your team needs specific courses — for example, only the Security & Compliance and Observability courses for a team that already has solid MLOps foundations — we can structure the licence accordingly. Contact us with your specific requirements and we will tailor the proposal. Note that the full specialization licence is typically more cost-effective per course than targeted selections for 3+ courses.
Pricing is in euros (EUR) as the base currency. We can invoice in USD, GBP, or other major currencies on request. Payment is annual, billed at contract start. We accept bank transfer, credit card, and can work with purchase order processes for larger organisations. VAT is applied based on your organisation’s location. A formal invoice with all required fields for your accounts payable team is provided.

Have specific requirements not covered here? Email our corporate team — we reply within 1–2 business days.

Get in touch

Ready to upskill your team?
Let’s talk.

  • 30-minute discovery call — we’ll discuss your team’s current MLOps maturity, the courses that will have the most impact, and a realistic timeline for the programme
  • Custom proposal within 48 hours — tailored to your team size, course selection, and any specific requirements (SSO, reporting format, curriculum mapping)
  • No commitment required — the proposal and discovery call are free. We’ll only ask you to commit once you’re confident the programme is the right fit
  • Flexible start — your team can begin with the 3 currently available courses immediately after contract signing, while the remaining courses continue to release
Prefer to reach us directly?
[email protected]
Response within 1–2 business days

Request a proposal Tell us about your team — we’ll be in touch within 1–2 business days.

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