Klarvo.

AI training for energy

Engineering and operations teams work with dense time-series data but don't know the tools and methods to analyze it. The data stays with the software, not the engineers.

▶ The solution

AI literacy for executives; Python, data analysis, statistical thinking, machine learning and deep learning for technical teams. Hands-on, and tailored to your team's level and your own data.

▶ Use case

Teams learn to load meter and equipment data in Python, visualize time series and catch anomalies statistically. A separate AI literacy session is held for managers.

▶ Data you need

An anonymized sample of meter readings, equipment sensor logs and maintenance history.

The industry's data landscape

In energy, data comes from meters, field equipment, maintenance logs, billing and customer systems. Time-series data is abundant and often high-frequency. The real challenge is turning it into a form the people making decisions can use.

Corporate training gives your team the knowledge to do data and AI work without depending on outsiders. There are two audiences: executives, who need to tell what is possible from what is hype, and technical teams, who will do the work themselves.

The math behind it

Precision = TP / (TP + FP) · Recall = TP / (TP + FN)

  • TP: cases the model correctly flags
  • FP: cases it flags by mistake
  • FN: cases it misses
A model that is "95% accurate" tells you little on its own [1]. The first thing teams learn in my trainings is to ask which error costs the business more, and to pick the metric accordingly.

How I work

  1. Before

    Level and goal.

    The participants' current level, and what they should be able to do afterwards, is made clear. Executives and technical teams get separate goals.

  2. Preparation

    Content and examples.

    The programme is shaped around the team's level. Examples and exercises are chosen close to the company's real problems.

  3. Training

    Hands-on sessions.

    More practice than lecture: participants write code, work with data and interpret results. Questions are answered through the company's own work.

  4. After

    Back to work.

    A concrete task or project proposal the team can apply right after. Where the learning will be used is clear from the start.

Full service details: Corporate trainings →

▶ Questions and answers

AI training for energy — what data do you need?

An anonymized sample of meter readings, equipment sensor logs and maintenance history.

When is this the wrong choice?

The training is just a line in the annual plan. A training nobody will apply afterwards produces nothing but certificates. You actually need a project. If the work needs to get done soon, doing the project may be faster than training the team.

How does this work start?

With a short intro call. I listen to what you need and tell you plainly whether your data is enough for the job and what the first step should be. I reply to every message within 24 hours.

▶ Concepts

Linked to Wikipedia and Wikidata.

▶ Sources

  1. [1]Wikipedia, Precision and recall, 2026-10. en.wikipedia.org/wiki/Precision_and_recall

▶ Intro call

Let's talk about your situation.

In a 30-minute call I listen to what you need and tell you plainly how I can help.

Book an intro call

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