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AI & Automation

AI for Telecom Networks

GenAI and machine learning for network operations — where AI already runs in your network, the methods that actually work on telecom data, the 5G core's own analytics function, and how to choose a first deployment that cannot hurt you.

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26 lessons≈3.8h of video6 modulesEarns TELCOMA Telecom AI Specialist

What you'll learn

  • Place AI in the network you already know — what it does today, the three words that carry the course, and why data quality decides everything.
  • Command the core ML method families — supervised, unsupervised, evaluation, drift, deep learning — each taught through a real network problem, never as abstract math.
  • Understand what large language models genuinely do, where they help a network operations team today, and how their failure modes are contained.
  • Know the 5G core's built-in analytics machinery — what the Network Data Analytics Function is, how one analytics round-trip flows, how it splits training from serving, and where analytics live beyond the core.
  • Connect the automation the industry already runs to the closed-loop, intent-driven automation AI enables — and fix who approves what.
  • Close with the judgment layer — accountable AI, securing the models themselves, the build-vs-buy call, and choosing a first deployment that cannot hurt you.

Module 1 — The AI toolbox, in network terms

4 lessons · 1 lab · ≈24 min

Place AI in the network you already know — what it does today, the three words that carry the course, and why data quality decides everything.

  1. 1
    Where AI already lives in your networkWatch free
    7:05
  2. 2
    Model, training, inference — the three words that carry the courseFree with an account
    6:54
  3. 3
    What networks actually log: counters, KPIs and tracesFree with an account
    6:51
  4. 4
    Rules vs learning: when a threshold beats a modelFree with an account
    7:09
  5. Rule or Model?Checkpoint

    Run five network cases through the three tests — does the pattern shift, is a miss lopsided, must you explain it — and decide honestly which earn a model.

    ~5 min

Module 2 — Machine learning through telecom cases

5 lessons · 1 lab · ≈35 min

Command the core ML method families — supervised, unsupervised, evaluation, drift, deep learning — each taught through a real network problem, never as abstract math.

  1. 1
    Supervised learning: predicting tomorrow's trafficRequires subscription
    6:57
  2. 2
    Unsupervised learning: finding the anomaly you didn't nameRequires subscription
    7:38
  3. 3
    Judging a model: precision, recall and the cost of a false alarmRequires subscription
    8:05
  4. 4
    Drift: when yesterday's model meets today's networkRequires subscription
    9:34
  5. 5
    Deep learning — and when you actually need itRequires subscription
    8:40
  6. Where Do You Set the Bar?Interactive lab

    Move the alarm threshold on 800 scored cells and watch precision, recall and the operating BILL move against each other — the cheapest setting is not the one with the best F1.

    ~7 min

Module 3 — GenAI and LLMs in operations

5 lessons · 1 lab · ≈35 min

Understand what large language models genuinely do, where they help a network operations team today, and how their failure modes are contained.

  1. 1
    What an LLM is — and what it is notRequires subscription
    7:42
  2. 2
    The ops copilot: tickets, runbooks and summariesRequires subscription
    10:10
  3. 3
    RAG: grounding the copilot in your network's truthRequires subscription
    9:17
  4. 4
    Agents: from answering to actingRequires subscription
    10:09
  5. 5
    Where GenAI fails in a network — and how to contain itRequires subscription
    9:40
  6. Contain the FailureCheckpoint

    Match five ways an assistant goes wrong to the control that actually holds each — including the one grounding cannot touch.

    ~5 min

Module 4 — NWDAF and the network's own data pipeline

4 lessons · 1 lab · ≈28 min

Know the 5G core's built-in analytics machinery — what the Network Data Analytics Function is, how one analytics round-trip flows, how it splits training from serving, and where analytics live beyond the core.

  1. 1
    NWDAF: the network's own analytics functionRequires subscription
    9:30
  2. 2
    The NWDAF round-trip: subscribe, collect, deliverRequires subscription
    8:05
  3. 3
    Inside NWDAF: training vs serving (MTLF and AnLF)Requires subscription
    7:36
  4. 4
    Analytics beyond the core: MDAF and where RAN intelligence livesRequires subscription
    7:17
  5. Trace the Round TripCheckpoint

    Rebuild the analytics round trip one arrow at a time, and work out what the last arrow really carries.

    ~4 min

Module 5 — From SON to closed-loop automation

4 lessons · 1 lab · ≈28 min

Connect the automation the industry already runs to the closed-loop, intent-driven automation AI enables — and fix who approves what.

  1. 1
    SON: the automation you already ownRequires subscription
    10:00
  2. 2
    The closed loop: observe, decide, act — and who approvesRequires subscription
    9:58
  3. 3
    Explainability and rollback: automation that explains itselfRequires subscription
    10:12
  4. 4
    "AI-native": what changes when the loop is the designRequires subscription
    11:06
  5. Where Does the Person Stand?Checkpoint

    Place six automations on the advise / approve / act-within-bounds dial — and find the two whose undo does not reach.

    ~5 min

Module 6 — Trust, governance and your first deployment

4 lessons · 1 lab · ≈26 min

Close with the judgment layer — accountable AI, securing the models themselves, the build-vs-buy call, and choosing a first deployment that cannot hurt you.

  1. 1
    Accountable AI: explainability, bias and the audit trailRequires subscription
    8:31
  2. 2
    Model security: poisoning, theft and adversarial inputsRequires subscription
    10:04
  3. 3
    Build vs buy — and the team's skills mapRequires subscription
    9:40
  4. 4
    Your first deployment: pick the use case that cannot hurt youRequires subscription
    10:25
  5. Pick the First DeploymentCheckpoint

    Judge four candidates against reversible, measurable, advisory and shadow-first — and see why the most valuable one is not where you start.

    ~5 min

New to the terminology? Look up any acronym in the telecom glossary.

Take the certification exam

Earns a certificate

42 questions · 60 min · 65% to pass. Score 65%+ to earn your TELCOMA Certified AI for Telecom Networks Specialist — a QR-verifiable certificate.

Not ready yet? Take a free 20-question mock exam first.

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Unlock every lesson in AI for Telecom Networks

Stream all 26 lessons, follow the 6-module path, and earn the TELCOMA Telecom AI Specialist certificate.

  • Every module unlocked
  • Labs & full-length practice exams
  • Verifiable certificate
  • TELCOMA since 2009
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