---
title: "scikit-learn | Skills We Assign For | Azendo"
description: "scikit-learn for classical machine learning — when simpler models win, and the Azendo roles assigned for data science."
url: "https://azendo.co/skills/scikit-learn/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# scikit-learn.

scikit-learn is a Python library for classical machine learning — regression, classification, clustering and the preprocessing around them. It covers the methods that solve most commercial prediction problems without a neural network.

## Where scikit-learn fits on a long engagement.

A large share of business prediction problems are best solved by a gradient-boosted tree on well-prepared tabular data. It trains in seconds, can be explained to a stakeholder, and frequently beats a neural network on the same dataset. Reaching for deep learning first is a common and expensive instinct.

The work that decides the outcome is feature preparation and honest evaluation, not model selection. Leakage between training and test data is the single most common reason a model performs brilliantly in development and disappoints in production.

## What an assigned team does with scikit-learn.

The temptation on a short engagement is to reach for the impressive model, because a simple one looks like insufficient effort. On a standing assignment nobody needs to perform sophistication, which is usually how the simpler and better answer gets chosen.

Where the model is going to be served continuously rather than run once, the operational half needs its own capacity, assigned under [MLOps engineering](https://azendo.co/services/mlops-engineering/) alongside the modelling.

## What we use scikit-learn for.

* Churn and propensity models Tabular prediction where the output feeds a commercial decision and has to be explainable.
* A defensible baseline A simple model established first, so any complex alternative has to prove it is actually better.
* Finding leakage Reviewing an existing model that performs suspiciously well, which is usually a data problem rather than a modelling triumph.

## How scikit-learn capacity is assigned.

Modelling of this kind is assigned under [data science](https://azendo.co/services/data-science/), and we will recommend the simpler model when it is the right answer.

## Roles we assign scikit-learn for

* [NLP Engineer AI engineering](https://azendo.co/services/ai-engineering/nlp-engineer/)
* [Data Scientist Data science](https://azendo.co/services/data-science/data-scientist/)
* [Machine Learning Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/machine-learning-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)
* [Data science](https://azendo.co/services/data-science/)
* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)
* [MLflow — skill we assign for](https://azendo.co/skills/mlflow/)
* [Kubeflow — skill we assign for](https://azendo.co/skills/kubeflow/)

## Tell us what your roadmap needs scikit-learn for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)