ODSC Europe 2020

Virtual AI Expo Hall

Access ODSC Main Keynotes, 14 talks over 2 days and Network 3,000 other peers for FREE

17 -18 September, 2020  •  Virtual 

Expo Talks
Partners
Networking Events
Attendees

UNDERSTAND THE APPLICATION OF AI IN THE REAL WORLD

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

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

2 Demo Theaters With our free Expert-led demos and Expo Hall Tutorials, learn how these platforms and products can accelerate adoption of data science and AI within your organization.  Understand the various AI adoption pathways in detail to decide on build vs buy decisions.
25 Virtual Booths Visit our 25 partner booths to learn the latest solutions in AI for the Enterprise from the most imporant players in the AI space. Technologies being showcased;  Auto ML, Data Labeling, DevOps, DataOps, Deep Learning, Cloud Computing, Image, Voice and Facial Recognition. 
Networking Meet and network with 3,000+ Data scientists and join the most influential Data Science community worldwide. Make professional relationships that can last forever by being part this unique community. 

AI Expo Pass

  • Access to ODSC Main Keynotes

  • Access to ODSC AI Expo

  • Access to 16 partner talks

  • Access to ODSC Conference Slack Channel

  • Access to ODSC Main Virtual Networking Reception

General Pass

  • Access to all main conference talks & Keynotes

  • Access to ODSC AI Expo

  • Access to 16 partner talks

  • Access to virtual events

  • Full Access to Networking events

Training Full Day Pass

  • Access to 42 Hands-on Training and Workshops

  • Access to all main conference talks & Keynotes

  • Access to ODSC AI Expo

  • Access to 16 partner talks

  • Access to virtual events

  • Full Access to Networking events

AI for Finance

AI for Marketing

AI for Healthcare

AI for Energy

AI for Biotech & Pharma

AI for Retail

AI for Climate

AI for Machines

AI Cyber & Fraud

AI for Manufacturing

Demo Theater Speakers


Europe Demo Theatre
17th Sept, Thursday
18th Sept, Friday
17th Sept, Thursday
18th Sept, Friday
10:30 - 10:56
First Aid Kit for Data Science: Keeping Machine Learning Alive

The world of Machine Learning and Artificial Intelligence is growing more rapidly than ever before. Creative AI ideas sprout at an unbeatable pace, organizations are exploring new emerging opportunities for Machine Learning and AI. While developments in Machine Learning and AI keep touching the edges of technology, the burning question remains: how to bring those ideas in action, and how to keep them alive? In this session, Véronique Van Vlasselaer will talk about the often-forgotten steps after model development: What is required to turn Machine Learning and AI into value? What does it mean when one talks about the deployment or operationalization of machine learning? How do we manage and govern machine learning models once they run in production? Véronique will show that Model Management & Governance should not be a burden for data scientists, but helps to ease the process to bring and keep Machine Learning and AI models alive.

First Aid Kit for Data Science: Keeping Machine Learning Alive image
Véronique Van Vlasselaer, PhD
Decision Scientist | SAS
10:30 - 10:55
Is Infrastructure Holding Back Adoption of AI at Scale?

Our surveys of enterprise machine learning specialists reveal that at least half of organizations believe their technology infrastructure isn’t prepared for the future demands that will be placed on it by the adoption of AI and machine learning at scale. They face challenges up and down the stack, from silicon, through storage, networking and compute bottlenecks as well as the key decisions around best execution venue – on-premises, in the cloud or hybrid. In this session Nick will examine why AI puts strains on infrastructure and how it can be overcome.

Is Infrastructure Holding Back Adoption of AI at Scale? image
Nick Patience
AI & Machine Learning Research Leader | 451 Research
11:00 - 11:25
Revision Control for Structured Data

Revision control for source code – and especially Git – has caused a great leap forward in software development and delivery. A similar revolution has not yet taken place in data. This talk will discuss the various open source databases that are approaching this problem (with a focus on TerminusDB and TerminusHub), the underlying architectures and challenges in building both a ‘Git for data’ and a ‘GitHub for data’. It will posit that to be a truly collaboration and distributed system, it must be: 1) decentralized 2) offline-first: work offline and then resync when online again 3) reliable: conflicts are handled properly 4) private: end-to-end-encrypted, if desired 5) efficient: only changes (diffs) to the data set are transmitted between participants 6) collaborative: multiple people can work on the same data set Finally the talk will look to the future and the dawn of CI/CD for data

Revision Control for Structured Data image
Gavin Mendel-Gleason
CTO | TerminusDB
11:00 - 11:25
eXplainable Predictive Decisioning: combine ML and Decision Management to promote trust on automated decision making

The increased demand for transparent, explainable decision making, that is accurate, consistent and effective, has never been greater. Legislations like GDPR are just a result of increasing concerns about privacy, safety and transparency in general. While AI/ML solutions are great at making sense of high volumes of data, the reasoning process for most of the generated analytic models is usually quite opaque. Decision Management on the other hand, is a discipline that aims to provide full transparency on the decision process, but requires formalization of knowledge into decisions/rules, using some form of knowledge engineering (automated or not). During this presentation, attendees will learn about a standards based, pragmatic approach to achieve the goals of eXplainable AI (XAI), combining decision models and analytic models. The approach promotes an effective method to increase transparency on automated decision making, without losing effectiveness. In particular, presenters will demo how PMML (Predictive Modelling Markup Language), a well established standard for the representation of predictive models generated using Machine Learning can be transparently combined with DMN (Decision Model and Notation), a Decision Modeling standard that defines a high level language for decision automation. Attendees will have the opportunity to learn how the combination of these two Standards enhances and creates a high level effective solution for AI which can be explained and trusted.

eXplainable Predictive Decisioning: combine ML and Decision Management to promote trust on automated decision making image
Matteo Mortari
Software Engineer | Red Hat
eXplainable Predictive Decisioning: combine ML and Decision Management to promote trust on automated decision making image
Daniele Zonca
Principal Software Engineer | Red Hat
11:30 - 11:55
Sports Analytics – Leveraging Open Source Technology to Improve Athlete Performance

Get a behind the scenes look at how NC State University Strength and Conditioning use HPCC Systems to run their data analysis project. Get a practical look at how each part of the system is set up and how it works from getting data into the HPCC Systems cluster, to getting data out into reports to give to coaches. Learn how HPCC Systems makes answering their sport and data science questions possible by digging in to the framework of how the system was built.

Sports Analytics – Leveraging Open Source Technology to Improve Athlete Performance image
Christopher Connelly
Lead Sport Scientist | NC State University
11:30 - 11:55
Build and Deploy Custom AI Predictive Models

In this session you will learn how to build and deploy your own AI Models. For the session we will be using AutoAI, a graphical tool that analyses your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem setting. Using AutoAI, you can build and deploy a machine learning model with sophisticated training features and no coding. We will use some public datasets to build and deploy two different model pipelines, and analyse each of these models.

Build and Deploy Custom AI Predictive Models image
Yamini Rao
Developer Advocate/Community Manager | IBM
12:00 - 12:25
Annotating Data with AI-assisted Labelling

Your machine learning model is your training data. Labelling, managing, and optimising your training data to effectively build AI applications is the most resource and time-consuming part – by orders of magnitude – of taking an AI project from concept to production. That’s why we’ve created Cord – a training data platform that helps you effectively build and scale your training data with AI-assisted labelling and more.

Annotating Data with AI-assisted Labelling image
Eric Landau
Co-Founder | Cord
14:00 - 14:25
VerticaPy Demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression

Deep-diving VerticaPy – Building a prediction churn model using random forest and logistic regression VerticaPy is a Python library that exposes sci-kit like functionality to conduct data science projects on data stored in Vertica – taking advantage of the platform fast queries, built-in analytics and machine learning capabilities. In this session, we will demonstrate how simpler and quicker are data exploration and preparation. We will then demonstrate how in-database machine learning helps evaluate and deploy models easily while showcasing a prediction churn example using random forest and logistic regression

VerticaPy Demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression image
Badr Ouali
Head of Data Science | Vertica
14:00 - 14:25
A Quick, Practical Overview of KNIME Analytics Platform

If you don’t know KNIME or if you have colleagues who should know about KNIME, then this webinar is the place to start! In this demo, we will cover the following aspects of KNIME Analytics Platform: A modern, open-source data science platform with a visual workflow editor that lets you focus on learning methods rather than learning the tool itself. An extremely wide range of data sources, tools, and methods – many based on leading open source projects – all within one platform. Software that is open source and free. No limitations on methods, data, or operating systems. A strong KNIME community to support you, including thousands of freely available working examples.

A Quick, Practical Overview of KNIME Analytics Platform image
Paolo Tamagnini
Data Scientist | KNIME
14:30 - 14:55
Creating Efficiency and Trust with MLOps

In this talk, you will learn about the two goals of MLOps: efficiency and trust. We will dive deeper into a number of effective ways to reach those goals. While MLOps is useful for almost every machine learning project, better collaboration is most valuable for larger teams while full lineage is most valuable for the financial and healthcare industries.

Creating Efficiency and Trust with MLOps image
Jan van der Vegt
CEO and data scientist | Cubonacci
14:30 - 14:55
Best Practices: Partnerships between ML/AI and Data Labeling Companies

In this demo I will be using a geospatial analytics use case to discuss best practices between ML/AI and Data Labeling companies. Key Takeaways: – How to prepare for an engagement – What questions to ask when vetting a company: – Annotation Tool – Data Labeling – What metrics you should consider

Best Practices: Partnerships between ML/AI and Data Labeling Companies image
Soo Yang
Solutions Architect | iMerit
15:00 - 15:25
Build Your Own Cloud Native Covid-19 data analytics with Kubernetes and OpenShift

We have seen a range of data published on the impact of various parameters on the spread of COVID-19, including population density, average number of people per household, ethnicity, weather data etc. Have you ever wanted to run your own analytics on covid-19 data, and examine data sets in order to draw a particular conclusion? Or possibly evaluate a theory, that may or not may not be true. Such analytics could potentially shed light on the impacts of various factors, and you can apply them to a variety of problems. In this talk we will demo an application, comprised of multiple microservices for parsing time series data for the number of positive and death cases in every country and region, and how environmental parameters could correlate with those figures. You will learn how to build your own data parser microservices, written in any programming language of your choice, and make them publicly available through REST APIs on any cloud platform with Kubernetes. You will also learn how to combine your Python application with other components developed with other tools and programming language. To showcase the capability of microservices, we will demo a sample application in which the data analytic application is written in Python, the data parser and frontend UI are written in Java, and the data visualization has been developed in Node.js.

Build Your Own Cloud Native Covid-19 data analytics with Kubernetes and OpenShift image
Dr. Mo Haghighi
Head of Developer Ecosystems, Europe | IBM
15:00 - 15:25
An Overview of Algorithmia: the Industry Leading Machine Learning Operations and Management Platform

Learn about Algorithmia’s machine learning operations and management platform that empowers teams to deploy models, connect to various data sources, automatically scale model inference, and manage the ML lifecycle in a centralized model catalog. We’ll demonstrate the process of deploying a sentiment analysis pipeline to Algorithmia that uses different languages and frameworks. Data science workflows include deploying a model, adding an OCR tool to the pipeline, testing a new version of the model, then calling the model from different languages. We’ll also discuss how Algorithmia handles the underlying MLOps infrastructure and operations related to security, scalability, and governance.

An Overview of Algorithmia: the Industry Leading Machine Learning Operations and Management Platform image
Kristopher Overholt
Sales and Solution Engineer | Algorithmia
15:30 - 15:55
Leverage Data Lineage to Maximize the Benefits of Big Data

Attend this session to understand how data lineage is helping companies to:

  • Decipher complex algorithms in systems that supply data to the “data lake”
  • Increase trust in data for analysts and data citizens/scientists
  • Manage the impact of application changes on downstream analytic systems
  • Simplify the consumption and understanding of data flow throughout the enterprise
  • Learn through various use cases how companies are taking advantage of lineage to realize these benefits.
Leverage Data Lineage to Maximize the Benefits of Big Data image
Ernie Ostic
Senior Vice President of Product | MANTA
15:30 - 15:55
Learn How to Seamlessly Use Julia for Your Machine Learning Tasks

Learn how to seamlessly use Julia for your machine learning tasks — even if they are within an air-gapped secure environment or require an entire cluster for their computation. Julia Computing’s products take the guesswork out of building scalable solutions and can be used by data scientists and engineers with little to no knowledge of how such systems need to be architected. Develop and collaborate on your pipelines locally and deploy them into a scalable robust on-demand cluster with a single click.

Learn How to Seamlessly Use Julia for Your Machine Learning Tasks image
Dr. Matt Bauman
Director of Applications Engineering | Julia Computing, Inc.
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AI EXPO PARTNERS

Partnering With ODSC

Last year, ODSC welcomed nearly 20,000 attendees to an unparalleled range of events, from large conferences to hackathons and small community gatherings.

WHO WILL ATTEND

Our Solution Showcase Expo hall gathers the executives, business professionals, experts, and data scientists transforming the enterprise with Artificial Intelligence

  • CEO, CDO, CIO, CTO and other C-Suite Executives

  • VP of Engineering, R&D, Marketing, Business Development.

  • Product Managers and VP of Product

  • Business Analysts, Data Analyst, Research Analysts, and Marketing Analysts  

  • VP of Engineering, VP of Development, VP of Data

  • IT and Software Managers and Team Leads

  • Software Architects, Engineers and Developers

  • Data Engineers, Database Admins, Data Infrastructure Experts

REGISTRATION

COMPLIMENTARY AND PAID EXPO HALL PASSES

 Free Solution Showcase Passes are available to qualified attendees 

Expo Hall & Accelerate AI  Passes

  • AI Expo Hall Pass
  • $79

    10% off Ends Friday
  • Access the Expo Hall over 2 days
  • Expo Hall Demos
  • Access to Day Networking Sessions*
  • Expo Hall Tutorials Track
  • Uses Cases Track
  • AI Biz 2 Day Pass
  • $469

    10% off Ends Friday
  • Access to Expo Hall over 2 days
  • Expo Hall Tutorials
  • Expo Hall Demos
  • Access to Day Networking Sessions*
  • Access to 48 Talks and AI Workshops Session on April 30th to May 1st
  • Invitation to Evening Network Receptions and Special Events
  • AI Biz 4 Day Pass
  • $969

    10% off Ends Friday
  • Access to Expo Hall over 4 Days
  • Expo Hall Tutorials
  • Expo Hall Demos
  • Access to Day Networking Sessions*
  • Access to 48 Talks and AI Workshops Session on April 30th to May 1st
  • Invitation to Evening Network Receptions and Special Events
  • Access to 80+ AI presentations at ODSC May 2nd to 3rd
  • Access to 50+ AI workshops at ODSC May 2nd to 3rd
  • Access to ODSC Main Networking Reception

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