WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
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.

Pause
Mute Enter Fullscreen
#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

More from World Congress 2022

What the heck do "declarative" and "reactive" actually mean? thumbnail
30:40
about 4 years ago

What the heck do "declarative" and "reactive" actually mean?

André Kovac

The Design and Evolution of C++ thumbnail
1:01:15
over 4 years ago

The Design and Evolution of C++

Bjarne Stroustrup

But, you're not Facebook thumbnail
31:12
over 4 years ago

But, you're not Facebook

Kristijan Ristovski

Communicate efficiently with Software Architecture Diagrams thumbnail
28:11
over 4 years ago

Communicate efficiently with Software Architecture Diagrams

Simon Lasselsberger

Get Ready for Jakarta EE 10! thumbnail
42:53
over 4 years ago

Get Ready for Jakarta EE 10!

Ivar Grimstad

Keep your code refactorable thumbnail
26:46
over 4 years ago

Keep your code refactorable

Gerrit Stapper

Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 16:50–17:20

Stage 7

You Can’t Rerun Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

September 25, 2026 · 10:20–10:50

Mainstage

How LinkedIn Turns AI Breakthroughs into member and customer value

Erran Berger, Frederic Lardinois

Erran Berger
Frederic Lardinois
Open session

World Congress 2026 North America

September 24, 2026 · 15:30–16:00

Tech Leaders Stage

Transforming Enterprise AI with Tabular Foundation Models

Leo Grinsztajn, Tom Palczewski, Yaad Oren, Ahmad Rushdi

Leo Grinsztajn
Tom Palczewski
Yaad Oren
Ahmad Rushdi
Open session

World Congress 2026 North America

September 25, 2026 · 16:10–16:40

Stage 2

Beyond SQL Generation: How to Teach Agents What Your Database Actually Means

Kris Jenkins

Host / Developer Voices

Kris Jenkins
Open session

World Congress 2026 North America

September 25, 2026 · 16:10–16:40

Stage 3

The Five Percent Club: The Culture and Technological Shift Behind Successful AI Deployments

Tara Hernandez

Tara Hernandez, VP of Developer Productivity at MongoDB

Tara Hernandez
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 5

Edge AI: Running Agentic Intelligence Where Internet Can't Reach

Nitin Eusebius

AWS - Principal Solutions Architect

Nitin Eusebius

Related videos

Getting Started with Machine Learning thumbnail
44:11
LIVE • almost 5 years ago

Getting Started with Machine Learning

Alexandra Waldherr

Overview of Machine Learning in Python thumbnail
57:46
LIVE • almost 3 years ago

Overview of Machine Learning in Python

Adrian Schmitt

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation thumbnail
21:17
World Congress 2024 • almost 2 years ago

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Fine-Tuning Small Language Models for Agentic AI thumbnail
29:18
World Congress 2026 Europe • 3 months ago

Fine-Tuning Small Language Models for Agentic AI

Björn Buchhold

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment thumbnail
25:41
World Congress 2024 • about 2 years ago

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Joy

Intelligent Automation using Machine Learning thumbnail
2:11:02
LIVE • over 5 years ago

Intelligent Automation using Machine Learning

Boris Krumrey, Andreas Palfi, Radu Pruna

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.