Machine Learning Consulting Services

Overview

Machine learning lets systems learn patterns from data and improve their predictions over time, without being explicitly programmed for every scenario. It falls into three broad types — supervised, unsupervised and reinforcement learning. Avocado builds ML models for organisations that need to forecast, classify or optimise — from budgeting and churn prediction to fraud detection and process optimisation — grounded in a governed data foundation, not spreadsheets and guesswork.

Who is this for?

You'll recognise the situation

You want to cut forecasting and budgeting cycles from months to days while improving accuracy.

You want to identify at-risk customers or revenue leakage before it shows up in the numbers.

You need to identify and prevent fraudulent or erroneous transactions.

You price loans, insurance or other risk-based products on static rules rather than each customer's actual risk.

You run asset-heavy operations where small gains in equipment availability are worth as much as new assets.

You want predictions your executives and SMEs can adjust and override, not a black box.

What are the three types of machine learning?

Three types, matched to the problem

Supervised learning

Learns from labelled historical examples to predict a known outcome. Used for forecasting, churn prediction, classification and event detection.

Unsupervised learning

Finds structure in data with no labels. Used for customer segmentation, market-basket analysis and anomaly or fraud detection.

Reinforcement learning

Learns through trial and error, supported by reward systems, which actions produce the best result over time. Used for process and traffic optimisation and resource allocation.

What's included?

Models you can trust and adjust

Featurisation & modelling workshops

Workshops with SMEs and executives to identify the predictors that actually matter for your business, not a generic template.

Model build & validation

Built and tested against your real historical data until it reaches a high level of accuracy before it's trusted for decisions.

Overlay & adjustment

Forecasts your board, executives and SMEs can still adjust — the model informs the decision, it doesn't replace judgement.

Granular, actionable output

Predictions at the level of detail you need — by SKU, by customer, by site — not just a headline number.

How do we deliver it?

Discover, prove, productionise, monitor

Discover

Gather and centralise the data, and run workshops with SMEs to understand the nuances and outliers. Builds on an AI Readiness & Feasibility Study if you've done one.

Prepare & model

Prepare targets, labels and features, then test and select the right model and algorithm for the problem.

Prove it

Train a small, auditable model on real data and tune it until there's confidence the outcome can be predicted, before investing in production.

Productionise

Production-grade pipelines integrated into the systems that act on predictions, with overlay capability for board, executive and SME adjustments.

Monitor & improve

Monitor for drift and retrain as new data and trends emerge, with MLOps that keep the model accurate after go-live.

What tools and technology do we use?

The stack behind it

Databricks and cloud-native ML services on Azure, AWS or GCP for training and deployment; Python and R for modelling; Power BI for delivering forecasts in a format executives actually use. Built on your AI-ready data layer so models train on governed, trustworthy data rather than raw exports.

What outcomes can you expect?

What you walk away with

Forecasting and budgeting effort cut by as much as 90% in comparable published examples, while improving accuracy and consistency.

Proactive identification of at-risk customers and revenue leakage instead of discovering it after the fact.

Predictions granular enough to act on — by SKU, by site, by customer — not just a top-line number.

How is this engagement structured?

Scoped individually based on the prediction problem and data available — typically starting with a short discovery phase before a full build. Contact us for a tailored quote.

Common questions

What is machine learning?

A set of techniques that let systems learn patterns from data and improve predictions over time — falling into supervised, unsupervised and reinforcement learning depending on the problem.

What's the difference between the three types?

Supervised predicts a known outcome from labelled examples (forecasting). Unsupervised finds structure with no labels (segmentation, fraud detection). Reinforcement learns by trial and error which actions work best (optimisation).

Do I need a data platform before this?

Yes — models are only as good as the data they train on. A governed Lakehouse and AI-ready data layer are the foundation this builds on.

How is this different from generative AI?

ML predicts or classifies based on patterns in your data (a forecast, a churn score, a fraud flag). Generative AI creates new content or holds a conversation. Often used together, but they solve different problems.

Can executives still adjust the predictions?

Yes — our models are built with overlay capability so board, executive and SME judgement can adjust the output rather than being locked out.