Nearly every organisation has adopted AI. Far fewer are profiting from it — the pilots work in the notebook and stall on the way to production. That gap is not an enthusiasm problem; it is a skills problem, and skills are learnable.
Two old Greek words name our answer. Mathesis is learning — the root of “mathematics”: that which can be learned. Praxis is that learning enacted — theory become practice. Praxis is VakeWorks’ AI-enablement service: we bring the mathematics and the engineering to your team as one craft, hands-on, on your own stack — and walk out with your people able to do the thing, not having watched slides about it.
What we actually do
- On-premises, expert-led workshops — one focused day or a weekly series, built for working professionals. Mathematical depth and production engineering taught together, because taught apart they don’t transfer. Delivered on your stack, with your data, culminating in a real artifact your team ships.
- The production layer, specifically — MLOps and reproducible pipelines, applied LLM and agentic engineering, feature stores and the training/serving path, evaluation and drift — the exact places pilots stall. Grounded in the open, openly-licensed ScaDaMaLe course book (free for self-study; the workshops are the guided, hands-on delivery).
- A track record — this material has been delivered to data scientists and engineers at a major Nordic bank and, on-site, to one of the world’s largest insurance brokerages, among others.
First, we measure — so the workshop hits the real gap
Before we teach, we map: point us at what your team already knows (or a programme’s materials) and we score it, concept by concept, against current industry demand — none / partial / strong, with the evidence shown for every call, no keyword-matching theatre. The workshop then targets the highest-value gap, not a generic syllabus. We’ll map one programme free as a taster, so you see the gaps before you decide anything.
Below is that instrument run on a synthesised, anonymised “generic Nordic MSc” — an illustration of what the diagnosis looks like (not a live client; the point is the method):
| Skill area | 2026 demand | Why it matters | Programme coverage | Covered by |
|---|---|---|---|---|
| MLOps / ML platform engineering | CRITICAL GAP | “Chronically undersupplied”; production pipelines, monitoring, CI/CD for ML (AI hiring reports 2026). | STRONG | ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| Applied LLM / GenAI engineering | CRITICAL GAP | Fastest-rising demand: RAG, agentic orchestration, LLM evaluation, guardrails (2026 talent reports). | STRONG | ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| Data engineering & modern infrastructure | HIGH GAP | Streaming, lakehouse, orchestration, data quality at scale; “60–70% of time on data plumbing” without it. | PARTIAL | ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| AI governance, safety & compliance | MODERATE | EU AI Act in force; auditing, compliance, safety engineering roles growing. | PARTIAL | Ethics & Responsible AI (elective), ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| Cloud & distributed computing for AI | HIGH GAP | Scaling training + inference: clusters, containers, GPU/accelerator infrastructure. | STRONG | ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| AI product management | CRITICAL GAP | Rare profile combining product judgment with ML trade-off understanding. | ABSENT | — |
| Software engineering for ML | HIGH GAP | “Production judgment over theoretical skill”: testing, versioning, reproducibility of ML systems. | STRONG | ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| Classical ML & statistical foundations | WELL COVERED | Still essential; the Nordic strength. | STRONG | Advanced Probability & Statistics, ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe), Foundations of Machine Learning, Probabilistic Graphical Models |
| Deep learning (core) | WELL COVERED | High steady demand. | PARTIAL | Deep Learning / Neural Networks |
| Computer vision | WELL COVERED | Steady demand. | PARTIAL | Computer Vision |
| NLP / language technology | MODERATE | Surging with GenAI, but classical NLP curricula lag the LLM era. | PARTIAL | NLP / Language Technology |
| Mathematical & theoretical foundations | WELL COVERED | Research-role backbone. | PARTIAL | Optimization, ScaDaMaLe — an open course (vake.works/courses/ScaDaMaLe) |
| Domain applications (health, bio, finance) | MODERATE | Domain-specific AI expertise in demand; uneven coverage. | ABSENT | — |
| AI literacy for non-specialists | MODERATE | Product/ops leaders increasingly need AI literacy. | PARTIAL | Ethics & Responsible AI (elective) |
Bring Praxis to your team
Move from pilot to production
Expert-led, hands-on AI workshops on your premises — the mathematics and the engineering as one craft, on your stack, ending in an artifact your people ship. One focused day or a weekly series. We map one programme free first, so the workshop targets your real gap.
The ScaDaMaLe Book
The workshops are built on our free, openly-licensed (CC BY-SA 4.0) course book — the mathematics and engineering of scalable, distributed, LLM-era machine learning, every lab runnable in Docker on your own laptop. Read it, self-study, then bring us in to go hands-on together.
Where mathesis becomes praxis.
