Flagship programme

Inference Telemetry for Product Teams

Eleven weeks to make neural inference readable inside your product analytics — without pretending every question has a metric.

Informational price: £1,240 per seat · No checkout on this site

Engineer reviewing telemetry on dual monitors

Who it is for

Product managers, analytics engineers, and ML-adjacent developers who already have (or will soon have) a neural feature in a consumer or B2B application. You should be comfortable reading a dashboard and collaborating across functions.

Learning outcomes

  • Design an inference event grammar tied to user outcomes
  • Build latency, confidence, and cost views that survive stakeholder review
  • Run a weekly decision ritual with clear owners and escalation paths
  • Document limitations so telemetry is not over-claimed

Modules

  1. Event grammar & session context

    Define request, model version, confidence band, and outcome fields. Map them to your existing analytics warehouse.

  2. Latency surfaces that product can read

    Percentiles, cold-start paths, and user-visible fallbacks — presented without drowning PMs in infrastructure jargon.

  3. Confidence drift & cohort views

    Histograms, segment contrasts, and when to escalate to qualitative research.

  4. Cost attribution across surfaces

    Tie inference spend to product areas; prepare for budget reviews without spreadsheet theatre.

  5. Decision rituals & capstone critique

    Ship a shared board and a 30-minute weekly agenda. Present to mentors and peers for structured critique.

Instructor

Portrait of Helen Marlowe

Helen Marlowe

Former analytics lead for a UK marketplace; now mentors cohorts on inference instrumentation.

Helen spent seven years bridging ML and product analytics, including two major ranking migrations. She teaches the flagship with guest operators from privacy and SRE backgrounds.

Informational pricing

£1,240 / seat

Includes live sessions, mentor feedback, and twelve months of alumni forum access. Team seats and Fleet Observatory coaching are arranged separately — see pricing.

Enquire about a seat

FAQ

Do I need to be a machine learning engineer?

No. You need familiarity with product analytics and enough technical partnership to change instrumentation. Pure research ML roles without product context often find the course too applied.

What tools do you require?

Any event pipeline and dashboard stack your team already trusts. We demonstrate patterns with SQL and common BI tools; we do not mandate a vendor.

What is a real limitation of this programme?

We do not teach model training or evaluation science in depth. If your primary problem is improving model accuracy rather than reading production behaviour, a research-focused programme is a better fit — we will say so in the intake call.

Is there a payment checkout here?

No. Pricing is informational. Enrolment is confirmed after a fit conversation and invoice.

Learner notes

“Week four’s confidence-band lab is what we still use. Capstone feedback was sharp — they pushed us to delete three charts that looked impressive but answered nothing.”

S. Okonkwo · Product Analytics · Birmingham

“Solid on latency. I wanted more Android sampling detail; the On-Device Signal Desk short course filled that gap afterward.”

Client in media · London