Feature Engineering and feature Selection Part 1

Pieter Buteneers,

Kacper Łukawski

k-NN on steroids – an introduction to approximate nearest neighbours

Kacper Łukawski, Qdrant

Threat modeling and security assessment for ML models

Threat modeling and security assessment for ML models,

Andreas Bühlmeier

How Machine Learning is (becoming) creative

Andreas Bühlmeier, DBC Enterprice Intelligence

Aditya Gulgani

State-of-the-art in large scale recommender systems

Aditya Gulgani, Meta

Michael Osterrieder

Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc

Workshop: Machine Learning 101++ Using Python

Pieter Buteneers,

Dr. Christian Winkler

How to turn large amounts of text into insights with machine learning

Dr. Christian Winkler, datanizing GmbH

Leonardo De Marchi

Workshop: Advancements in Natural Language Processing (NLP)

Leonardo De Marchi, idea.io

Lisa Becker

The importance and pitfalls of pseudonymization

Lisa Becker, ML6

Feature Engineering and feature Selection Workshop Part 2

Pieter Buteneers,

Christoph Henkelmann

Sebastian Meyen

Welcome to the first day of the ML Con/ IoT Con and opening keynote: Brains vs. Brawn – Is endless scaling really the way forward?

Christoph Henkelmann, DIVISIO GmbH

Sebastian Meyen, Software & Support Media

Oliver Neubauer

Automating inspections with smart drones

Oliver Neubauer, TOP seven GmbH & Co. KG

Serg Masis

How XAI will quietly revolutionize AI

Serg Masis, Syngenta

Dr. Michael Sperber

Mirko Ross

IoT and Sustainability

Mirko Ross, asvin.io

Nikolay Manchev

Underspecified pipelines: Why good models underperform in production

Nikolay Manchev, Domino Data Lab

Hauke Brammer

Kotlin? For Machine Learning?

Hauke Brammer, DeepUp GmbH

Dr. Rachid Kherrazi

Why Security is Important in ML and How to Secure your ML-based Solutions

Dr. Rachid Kherrazi, AKKA Netherlands B.V.

Håkan Silfvernagel

Machine Learning on the Edge using TensorFlow Lite

Håkan Silfvernagel, Miles Oslo AS

Dr. Tim Frey

How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG

Hauke Brammer

Keep an eye on AI — Monitor ML models in production

Hauke Brammer, DeepUp GmbH

Jon Bratseth

Vector embedding AI in production systems

Jon Bratseth, Yahoo

Amit Bendor

Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist

Dorra Nouira

Using A.I to make recommendations for career progression

Dorra Nouira, Boostrs

Laura Kölbl

Nature inspired optimization algorithms / Algorithm-based Staffing with Ant Colony Optimization

Laura Kölbl, MATHEMA GmbH

Lars Gregori

Honey, I shrunk the TinyML

Lars Gregori, SAP CX

Johannes Hötter

Greenfield and Brownfield Data Labeling – from Idea to Production with data-centric AI

Johannes Hötter, onetask.ai // kern.ai

Danilo Dresen

IoT Analytics at Scale – Customer based product development

Danilo Dresen, taod Consulting GmbH

Ioannis Tsamardinos

How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio

Adrian Boguszewski

Optimize your network inference time as a data scientist, not as a web developer

Adrian Boguszewski, Intel

Self-supervised Learning: Teaching AI common sense

Pieter Buteneers,

Dr. Christian Winkler

Introduction to Sentence Embeddings

Dr. Christian Winkler, datanizing GmbH

Julien Schuermans

The unreasonable effectiveness of simple models

Julien Schuermans, ML6

MLCON 2022 Munich ARCHIVE

DON'T MISS ANY ML CONFERENCE NEWS!