ODSC EUROPE 2023 | IN-PERSON & VIRTUAL CONFERENCE

AI EXPO & DEMO HALL

AI Solutions Showcase | Networking

Tobacco Dock, London  –   June 15th-16th, 2022

meet our 2022 partners
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TALKS & DEMOS
AI Solution Providers
Networking Events
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Learn how to Build AI Better

Want to keep up with the latest AI developments, trends, and insights? Dealing with the build or buy dilemma to grow your business? Seeking to interact with data-obsessed peers and build your network? 

Look no further: The ODSC AI Expo & Demo Hall is the right destination for you

Keynotes

 We will have some of the best and brightest minds speaking at ODSC Europe 2022. Attend keynotes talks presented by a line-up of visionaries and thought leaders in industry, research and academia.

Product Demo Talks

Attend 20+ sessions on AI for the Enterprise; AI for Finance, AI for Healthcare and much more to understand the various AI adoption pathways in detail and pros and cons of build vs buy decisions.

AI Solutions

Visit our partners’ booths to learn about the latest solutions in AI for the Enterprise from the most important players in the AI space. Auto ML, Data Labeling, DevOps, DataOps, Deep Learning, BI Platforms and much more.

Networking

Meet and network with over 1,500 data science and AI experts and join the most influential Data Science community worldwide. Make lasting professional relationships by being part of this unique community.

Expo Speakers
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Expo Hall Topics

Partner sessions offer compelling insights on how to make data science and AI work for your industry. Here are some of the topics you can expect at the AI Expo & Demo Hall. The full agenda is coming soon. Register now

Machine Learning & Deep Learning Platforms

MLOps & Data Infrastructure

Data Annotation & Data Labeling

Data Analytics & Decision Intelligence Systems

Machine Learning on the Cloud & Big Data

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Access to Hands-on ODSC Training Sessions | (Wed-Thur)
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Access to Virtual Conference recordings (100+ sessions)
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Virtual Demo Talks & Ai Expo (2 Days Wed-Thu)
Access to Virtual AI Expo Hall (15+ Partners)
Access to Virtual Demo Talks (Wed - Thur)
Access to Virtual Keynotes
Access to Virtual Talks & Workshops
Access to Virtual Hands-on Training Sessions (30+ Sessions) | (Wed-Thur)
Access to Virtual Conference recordings (100+ sessions)
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£124 + VAT
Virtual Workshops & Talks (2 Days Wed-Thu)
Access to Virtual AI Expo Hall (15+ Partners)
Access to Virtual Demo Talks (Wed - Thur)
Access to Virtual Keynotes
Access to Virtual Talks & Workshops
Access to Virtual Hands-on Training Sessions (30+ Sessions) | (Wed-Thur)
Access to Virtual Conference recordings (100+ sessions)
Virtual Platinum Pass
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Virtual Training, Workshops, & Talks (2 Days Wed-Thu)
Access to Virtual AI Expo Hall (15+ Partners)
Access to Virtual Demo Talks (Wed - Thur)
Access to Virtual Keynotes
Access to Virtual Talks & Workshops
Access to Virtual Hands-on Training Sessions (30+ Sessions) | (Wed-Thur)
Access to Virtual Conference recordings (100+ sessions)

*Learn more about what is included in the Standard+ Pass here

Visionaries and Thought Leaders

With an AI Expo Pass, you can take advantage of 40+ demo sessions and ODSC Keynotes. Our Speakers will provide compelling insights on how to make data science and AI work for your industry. 

Past Keynote Speakers

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Pprevious Expo Speakers


Europe 2022 AI Expo Hall Schedule

We are delighted to announce our Europe 2022 Preliminary Schedule! More sessions coming soon! 
Keynotes & Demo Talks Schedule – BST (UTC +1)
--Wednesday, 15th June
-Thursday, 16th June
--Wednesday, 15th June
-Thursday, 16th June
09:40 - 10:10
ODSC Keynote: Physics-inspired Learning on Graph

Keynote | Virtual | Machine Learning | Deep Learning | All Levels

 

The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. I argue that the very “node-and-edge”-centric mindset of current graph deep learning schemes may hinder future progress in the field. As an alternative, I propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML…more details

ODSC Keynote: Physics-inspired Learning on Graph image
Michael Bronstein, PhD
DeepMind Professor of AI | Head of Graph Learning Research | University of Oxford | Twitter
10:00 - 10:30
Best Practices of Effective ML Teams

Demo Talk | In-person | MLOps & Data Engineering | All Levels

 

What does it take to get the best model into production? We’ve seen industry-leading ML teams follow some of the same common workflows for dataset management, experimentation, and model management. I’ll share case studies from customers across industries, outline best practices, and dive into tools and solutions for common pain points…more details

Best Practices of Effective ML Teams image
Allan Stevenson
Success ML Engineer, EMEA | Weights and Biases
10:40 - 11:10
Run Azure Machine Learning Anywhere in Multi-cloud or on Premises ​

Demo Talk | Virtual | MLOps and Data Engineering | All Levels

 

In this session, you will learn how to run machine learning workloads with seamless Azure Machine Learning experience anywhere, including on-premises, in multi-cloud environments, and at the edge. Use any Kubernetes cluster and extend machine learning to run MLOps, model training, real-time inference or batch-inference. You can manage all the resources through a single pane with the management, consistency, and reliability.​..more details

Run Azure Machine Learning Anywhere in Multi-cloud or on Premises ​ image
Doris Zhong
Product Manager | Microsoft
11:00 - 11:30
Introduction to WSL2 for Data Science with Z by HP

Demo Talk | In-person | Machine Learning | All Levels

 

WSL 2 – Windows Subsystem for Linux is a layer for running Linux binary executables natively on Windows. What is WSL 2? How does it fit within your workflow? What is the value of it for data science? How to setup your machine? How to run your first code? This introductory session aims to provide answers to these questions, get you introduced to WSL2 and get you started by configuring your machine and running your first code…more details

Introduction to WSL2 for Data Science with Z by HP image
Akram Dweikat
Network Economics (ML), Deliveroo | Global Data Science Z by HP Ambassador
11:20 - 11:50
Graph Embeddings: 5 Ways Your AI Can Learn From Your Connected Data

Demo Talk | Virtual | Machine Learning | All Levels

 

Graphs can represent almost any kind of data, from complex supply chains, medical research, customer 360, and fraud detection.

Implemented in production-grade within the Neo4j Graph Data Science library, Graph Embeddings are an advanced AI technology used to translate your connected data – knowledge graphs, customer journeys, and transaction networks – into a predictive signal.

Applications of Graph Embeddings are numerous: finding fraud, entity resolution and disambiguation, improving product recommendations, discovering new drugs and predicting churn…more details

 

Graph Embeddings: 5 Ways Your AI Can Learn From Your Connected Data image
Nicolas Rouyer
Senior PreSales Consultant | Neo4j
11:40 - 12:10
“It worked on my Laptop, now what?” Using OS tool MLRun to Automate the Path to Production

Demo Talk | In-person | MLOps and Data Engineering | All Levels

 

MLRun is an open-source MLOps orchestration framework. It exists to accelerate the integration of AI/ML applications into existing business workflows. MLRun introduces Data Scientists to a simple Python SDK that transforms their code into a production-quality application. It does so by abstracting the many layers involved in the MLOps pipeline. Developers can build, test, and tune their work anywhere and leverage MLRun to integrate with other components of their business workflow…more details

“It worked on my Laptop, now what?” Using OS tool MLRun to Automate the Path to Production image
Gilad Shaham
Director of Product | Iguazio
11:40 - 12:10
A Graph Data Science Framework for the Enterprise

Demo Talk | In-person | Machine Learning | All Levels

 

You’ll leave this demo with essential resources for better predictions and outcomes using the data you already have through graphs and Neo4j…more details

A Graph Data Science Framework for the Enterprise image
Kristof Neys
Data Science Specialist | Neo4j
11:50 - 12:30
Predicting the Next NBA Champion with Cloudera’s Applied ML Prototypes (AMPs)

Talk | In-person | MLOps and Data Engineering | All Levels

 

In this session, Jake Bengtson from Cloudera will demonstrate how the AMP Churn Modeling with scikit learn can be repurposed to create a web application that will predict this year’s NBA champion. From ingesting historical NBA data to altering the existing Flask application to use a newly trained model, we will walk through the entire process of going from AMP to MVP…more details

Predicting the Next NBA Champion with Cloudera’s Applied ML Prototypes (AMPs) image
Jake Bengtson
Senior Product Marketing Manager | Cloudera
12:00 - 12:30
Introduction to WSL2 for Data Science with Z by HP

Demo Talk | Virtual | Machine Learning | All Levels

 

WSL 2 – Windows Subsystem for Linux is a layer for running Linux binary executables natively on Windows. What is WSL 2? How does it fit within your workflow? What is the value of it for data science? How to setup your machine? How to run your first code? This introductory session aims to provide answers to these questions, get you introduced to WSL2 and get you started by configuring your machine and running your first code…more details

Introduction to WSL2 for Data Science with Z by HP image
Akram Dweikat
Network Economics (ML), Deliveroo | Global Data Science Z by HP Ambassador
12:20 - 12:50
Building the Best AI Infrastructure Stack to Accelerate Your Data Science

Demo Talk | In-person | MLOps and Data Engineering | All Levels

 

In this talk, we’ll tackle the challenge of optimizing the AI infrastructure stack using Kubernetes, NVIDIA GPUs, and Run:ai. Walking through an example of a well-architected AI Infrastructure stack, we’ll discuss how Kubernetes can be augmented with advanced GPU scheduling to maximize efficiency and speed up data science initiatives…more details

Building the Best AI Infrastructure Stack to Accelerate Your Data Science image
Robert Magno
Sales Engineer | Run:Ai
13:10 - 13:40
Is Poorly Labelled Data the Culprit for Failed AI Projects?

Demo Talk | In-person | All Levels

 

In this talk we will examine the key phases on AI solution development and the critical aspects of humans in the loop (HITL) to ensure their success. From data cleansing to annotation to quality control, HITL has been shown to improve ML/AI project quality, while improving efficiency and accelerating time to value across multiple industries, including finance, retail, agriculture and more. We will share how the right combination of people and technology can be effectively paired with insight from an experienced CloudFactory tooling partner, and share an actual use case of this having been applied to a recent medical AI project…more details

Is Poorly Labelled Data the Culprit for Failed AI Projects? image
Andy Minshall
Head of Client Success, EMEA | Cloudfactory
Is Poorly Labelled Data the Culprit for Failed AI Projects? image
Ulrik Stig Hansen
Co-Founder | Encord
13:30 - 14:00
The Rapid Evolution of the Canonical Stack for Machine Learning

Demo Talk | Virtual | MLOps and Data Engineering | All Levels

 

Just a few years ago every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon, rolled their own AI/ML tech stack from scratch. Fast forward to today and we have a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? In order for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast…more details

The Rapid Evolution of the Canonical Stack for Machine Learning image
Lee Baker
General Secretary | AI Infrastructure Alliance
13:35 - 14:05
Is Reinforcement Learning the Right Tool for Your Problem?

Demo Talk | Virtual | All Levels

 

Reinforcement Learning (RL) is a technique for solving sequential decision-making problems that achieved superhuman performance in games such as Chess and GO. In recent years, RL algorithms have matured and are readily been applied in diverse contexts such as robotics, urban planning, healthcare, logistics, recommendation systems, etc. However, successfully training RL systems remains an art, and several considerations govern if RL is the right tool for a new problem. In this talk, we will discuss these considerations, overview some successful applications, and highlight major open issues and some dirty secrets behind the practice of RL…more details

Is Reinforcement Learning the Right Tool for Your Problem? image
Prof. Pulkit Agrawal
Assistant Professor | MIT
14:00 - 14:30
Data-centric AI: The Winning Combination of Human-in-the-Loop Real-World Data and Synthetic Data for Edge Cases

Demo Talk | In-person | Deep Learning | Machine Learning | All Levels

 

A case study of efficiently solving a real-world computer vision problem using a combination of labelled real-world data and synthetic data, combining the strengths of each data type. It considers best practices for combining the datasets and showcases the benefits of a platform approach using Appen's platform for real world sourcing and labelling and the Mindtech Chameleon platform to generate the synthetic data…more details

Data-centric AI: The Winning Combination of Human-in-the-Loop Real-World Data and Synthetic Data for Edge Cases image
Romain Chapman
Senior Manager, Business Development UK & Europe | Appen
Data-centric AI: The Winning Combination of Human-in-the-Loop Real-World Data and Synthetic Data for Edge Cases image
Dimitris Trigkakis
Machine Learning Engineer | Mindtech Global
14:10 - 14:40
The Hidden Layers of Tech Behind Successful Data Labeling

Demo Talk | Virtual | MLOps and Data Engineering | All Levels

 

Failure or delays in creating training data and deploying data ops can suffocate good deep learning models, a chance data scientists can’t bet on. In this session, attendees will learn how iMerit is solving the problem of scaling data pipelines with accuracy using unique technology. Join iMerit’s VP of Product, Glen Ford, as he uncovers the invisible technology building successful data labeling workflows and discovering anomalous and novel classes for customers using iMerit’s Edge Case technology…more details

The Hidden Layers of Tech Behind Successful Data Labeling image
Glen Ford
VP of Product | iMerit
14:30 - 15:00
Optimizing Your Analytics Life Cycle with Machine Learning and Open Source

Demo Talk | In-person | Machine Learning | All Levels

 

In this talk, data scientist Kyriakos Fistos, will take you through every step of the analytics journey throughout the analytics life cycle by showing how to prepare and visualize data, how to develop Machine Learning models, how to integrate Open Source models in your projects, and how to deploy and manage all of your analytical assets…more details

Optimizing Your Analytics Life Cycle with Machine Learning and Open Source image
Kyriakos Fistos
Data Scientist | SAS Institute
14:40 - 15:10
Simplifying Model Production with MLFlow Pipelines and Delta

Demo Talk | In-person | MLOps and Data Engineering | All Levels

 

Deploying models in to production is still the hardest problem in Data Science, but not if you use the right tools. In this session we look at how MLFlow pipelines simplify the process of deploying models to production. We look at the challenges of data management and how Delta can be used to ensure model are reproducible, without taking many replicas of your dataset…more details

Simplifying Model Production with MLFlow Pipelines and Delta image
Terry McCann
Director of AI | Advancing Analytics
14:50 - 15:20
AI for Emergency Response

Demo Talk | Virtual | MLOps and Data Engineering | Responsible Ai

 

In this talk I will describe the technical challenges in processing vast amounts of heterogeneous, noisy data in real-time from the web and other sources, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in humanitarian and emergency response. I will give specific examples and discuss relevant future research directions in several AI fields…more details

AI for Emergency Response image
Aoife Cahill, PhD
Director, AI Research | Dataminr
15:30 - 16:00
Data-driven ML Retraining with Production Insights

Demo Talk | In-person | MLOps and Data Engineering | All Levels

 

In this talk, we’ll showcase, through ML monitoring and notebooks, how data scientists and ML engineers can leverage ML monitoring to find the best data and retraining strategy mix to resolve machine learning performance issues. This data-driven, production-first approach enables more thoughtful retraining selections, shorter and leaner retraining cycles, and can be integrated into MLOps CI/CD pipelines for continuous model retraining upon anomaly detection…more details

Data-driven ML Retraining with Production Insights image
Oryan Omer
Lead Software Engineer | Superwise
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Thank You to All ODSC Europe Partners!

ODSC is proud to partner with numerous industry leaders providing organizations with the tools to accelerate digital transformation with AI. You can reach out to our Expo partners prior to the event for more information. Register now

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Who Should Attend?

The AI Expo & Demo Hall gathers executives, business professionals, experts, and data scientists who are transforming the enterprise with Artificial Intelligence. 

Business Leaders and Executives: Chief Data Scientists, Chief AI Officers, CDO, CIO, CTO, VPs of Engineering, R&D, Marketing, Business Development, Product, Development, Data

Directors of Data Science, Data Analytics Managers, Heads of Data and Innovation; Software, IT, and Product Managers

Data Science Professionals: Data Scientists, Data Engineers, Data Analysts, Architects, ML and DL Experts, Database Admins

Software Development Experts: Software Architects, Engineers, and Developers

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Past Companies in Attendance

Connect with like-minded professionals to learn about the latest languages, tools, and frameworks related to all types of streaming media applications. Here’s a sampling of companies that have attended Streaming Media Connect events.

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