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World Congress 2022 • Jun 15, 2022

What is relational learning and why does it matter?

Alexander Uhlig

Are you still wasting weeks manually engineering features for complex enterprise databases? Relational learning automates this bottleneck using statistical optimization to unlock rapid, end-to-end predictive analytics.

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#1 about 4 min

Introduction to data transformations for machine learning

Most machine learning models require data scientists to perform simple transformations to create consistent input vectors.

#2 about 3 min

The challenge of predicting with relational data

Relational databases with one-to-many relationships resist standard preprocessing techniques and fixed input vector formatting.

#3 about 3 min

The limitations of manual feature engineering cycles

Relying on domain experts and manual aggregation leads to inefficient, week-long development cycles for complex predictions.

#4 about 2 min

Automating feature engineering for relational datasets

New algorithms can automatically learn features from relational data to provide end-to-end automatization of predictive analytics.

#5 about 5 min

Analyzing brute-force feature learning and propositionalization

Traditional brute-force approaches apply numerous aggregations to columns but are inefficient and produce unhelpful garbage features.

#6 about 2 min

Supervised search algorithms for feature optimization

Supervised learning techniques statistically optimize the search for the best merge and aggregate operations across datasets.

#7 about 5 min

Iterative feature learning using conditional multirel logic

Generalizing decision tree patterns to relational data iteratively adds conditions to directly improve a loss function.

#8 about 1 min

Python API integration for relational machine learning frameworks

Data scientists can use a familiar Python pipeline structure to efficiently define feature learners and predictors.

#9 about 2 min

Getting started with open-source fast-prop implementations

Options for immediate integration include existing tools or the highly efficient fast-prop implementation for rapid propositionalization.

#10 about 1 min

Deep neural networks versus relational data structures

Deep neural networks require fixed-length input vectors, making them incompatible with the unbound relationships of relational data.

#11 about 2 min

Iteratively updating loss functions across relational parameters

Iteratively updating the loss function evaluates the impact of minor condition variations across a massive feature space.

#12 about 3 min

Supervised search space versus brute-force calculation and pruning

Defining a localized search space around data joins proves much faster than calculating billions of feature combinations.

Matching moments

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

1:03 min

Revolutionizing database search queries with language models

Calvin Seward Calvin Seward · Europe 2026 Virtual

11:07 min

Designing an automated and orchestrated machine learning target process

Simon Stiebellehner · World Congress 2021

1:49 min

Engineering scale-out architectures specifically for relational queries

Wei Hu Wei Hu · World Congress 2024

3:06 min

Storing machine learning data with specialized vector databases

Mary Grygleski Mary Grygleski · LIVE

2:29 min

Leveraging artificial intelligence to clean legacy data

Stephanie Cohen Stephanie Cohen +4 · World Congress 2026 Europe

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