Tomasz Hamerla

Tomasz Hamerla

at the intersection of software & data engineering

Tomasz Hamerla

2-Minute Read

The EU has moved from just regulating data via GDPR to implementing different laws that affect the machine learning pipeline at different stages. How that’s going to look like? Let’s take a look!

Defining the machine learning pipeline

Before diving deeper, let’s assume the following definition of a machine learning pipeline. The goal of this definition isn’t to provide a definitive list, but a model that can be referenced later on - like the OSI model, but for the machine learning.

  1. Project: Scoping the problem
  2. Data: Defining, Cleansing, Ingestion, Analysis, Validation, Feature Engineering, Splitting
  3. Model: Building, Training, Validation
  4. Deployment: Serving, Monitoring, Finetuning

Pipeline: the project layer

  • Regulating AI systems based on the risk they pose: new law - AI Act

Pipeline: the data layer

Pipeline: the model layer

Unlike at the data layer, where EU is trying to establish a single market for all data - the model layer itself seems to stay largely unregulated.

  • How do you share the model with other companies?
  • How do you train on 3rd party models?

Those questions remain not explicitly answered. The single market for the model is not here.

Pipeline: the deployment layer

  • Regulating single market in the EU: new laws - Digital Services Act, Digital Markets Act
  • Holding accountable for the harm caused by AI systems: new law - AI liability directive

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