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AI for the enterprise

Machine learning & forecasting

Models that learn from your history to predict demand, risk, churn and more.

  • Predictive AI
  • Typical project: 4–10 weeks

Overview

Classic machine learning predicts a number or a category from data: next month's demand, the chance a customer leaves, whether a payment is fraud. It is mature, measurable and often worth more than a chatbot.

Success depends on data quality and on wiring each prediction into a decision someone actually makes, such as a reorder, an offer or a review queue.

Names you'll meet

  • Python
  • PyTorch
  • TensorFlow
  • Databricks
  • Amazon SageMaker

What's inside

  1. 1

    Data & features

    History turned into signals the model can learn from.

  2. 2

    Training

    Algorithms fitted and compared on past data.

  3. 3

    Deployment

    Predictions served in batches or in real time.

  4. 4

    Monitoring

    Watch accuracy and drift; retrain on a schedule.

Is it right for you?

Best for

  • Demand and sales forecasting
  • Fraud, credit and churn risk
  • Pricing and recommendations

Think twice if…

  • You have little or poor historical data
  • No process will act on the predictions

Typical projects

  1. Proof of value on one prediction4–10 weeks
  2. Production forecasting model2–6 months
  3. Model monitoring and retrainingOngoing · 3–12 months

Typical ranges, not quotes. Scope, data and team size move them most.

Roles you'll hire

  • Data scientist
  • ML engineer
  • Data engineer
Browse available talent

Questions to ask a vendor

  1. 1Which baseline will you beat, and by how much?
  2. 2How will predictions reach the people or systems that act on them?
  3. 3How will you monitor drift?
  4. 4Who owns the model after handover?

Find Machine learning partners

Compare IT service companies with sourced, labelled facts, or post your project and get matched.