Register your interest for 2024
Register your interest for 2024

Machine Learning & Deep Learning

The Machine Learning & Deep Learning Conference track as part of ODSC Europe 2023 is where experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.

Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they demonstrate and teach the latest developments in Machine Learning and Deep Learning that solve problems in business and society today.

Register your interest for 2024

What You'll Learn

Topics

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

  • Neural Networks

  • Natural Language Processing

  • Computer Vision

  • Pattern Recognition

Tools & Languages

  • Python and R

  • Python SciPy, Pandas, etc

  • Scikit-learn

  • Tensorflow

  • Spark

  • Kubernetes and Kubflow

  • and more….

Frameworks

  • Tensorflow & PyTorch

  • Keras and Pylearn2

  • Theano

  • Caffe

  • Torch

  • Azure Machine Learning API

  • and more..

Past Machine Learning and Deep Learning Speakers

FounderFire Plus Algebra

Alan Rutter

Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Riskified,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.

Data Communication in the Age of AI(Talk)

Senior Research Associate in Biomedical Data Science and University Research LecturerUniversity of Oxford

Sara Khalid

Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.

Blue Collar ML ArchitectFreelancer

Oliver Zeigermann

Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.

MLOps: Monitoring and Managing Drift(Training)

 

Co-founder and Chief Product OfficerPredibase

Devvret Rishi

Dev is co-founder and Chief Product Officer for Predibase, a company looking to redefine how data scientists and engineers build models with a declarative approach. Prior to Predibase, he was a ML PM at Google working across products like Firebase, Google Research and the Google Assistant as well as Vertex AI. While there, Dev was also the first product manager for Kaggle – a data science and machine learning community with over 8 million users worldwide. Dev’s academic background is in computer science and statistics, and he holds a masters in computer science from Harvard University focused on machine learning.

The Next Generation of Low-code Machine Learning(Talk)

Principal Engineer, Threat ResearchImperva

Ori Nakar

Ori Nakar is a principal cyber-security researcher, a data engineer, and a data scientist at Imperva Threat Research group. Ori has many years of experience as a software engineer and engineering manager, focused on cloud technologies and big data infrastructure. Ori also has an AWS Data Analytics certification. In the Threat Research group, Ori is responsible for the data infrastructure and involved in analytics projects, machine learning, and innovation projects.

Botnets Detection at Scale – Lesson Learned from Clustering Billions of Web Attacks into Botnets(Talk)

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Senior Solutions Architect for AI & Machine LearningAWS

Heiko Hotz

Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.

Implementing Generative AI in Organisations: Challenges and Opportunities(Tutorial)

Head of Developer RelationSeldon

Ed Shee

Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.

Avoiding Crisis With Model Explainability(Talk)

Senior Software EngineerAnyscale Inc.

Kai Fricke

Kai Fricke is a senior software engineer at Anyscale. As a core maintainer of the Ray AI Runtime he is building software for distributed machine learning training and tuning. During his postdoc at Cambridge he utilized reinforcement learning to optimize large graph structures and co-authored two open source reinforcement learning libraries.

Scaling AI/ML Workloads with Ray(Talk)

Principal Data EngineerThoughtworks (Spain)

Shawn C. Kyzer

Shawn is passionate about harnessing the power of data strategy, engineering and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 15 years experience, Shawn removes technology as a barrier, and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics which sets him apart from the crowd.

Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients ranging from secret clearance level government organizations to Fortune 500 companies.

Turbocharge your Data Analytics Plane with AI(Workshop)

Associate Director - ML/AI, Computer VisionMSD

Guglielmo Iozzia

Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way). He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.

He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark

The Importance of Domain Specific LLMs and the Engineering Needed to Deploy Them in Your Own Corporate Environment(Tutorial)

Customer Success Manager and Value EngineerTangent Works

Philip Wauters

Philip Wauters is Customer Success Manager and Value engineer at Tangent Works working on practical applications of time series machine learning at customers from various industries such as Siemens, BASF, Borealis and Volkswagen. With a commercial background and experience with data engineering, analysis and data science his goal is to find and extract the business value in the enormous amounts of time-series data that exists at companies today.

Learn how to Efficiently Build and Operationalize Time Series Models in 2023(Workshop)

The Tangent Information Modeler, time series modeling reinvented(Solution Showcase)

Abstract:

Modeling time series data is difficult due to its large quantities and constantly evolving nature. Existing techniques have limitations in scalability, agility, explainability, and accuracy. Despite 50 years of research, current techniques often fall short when applied to time series data. The Tangent Information Modeler (TIM) offers a game-changing approach with efficient and effective feature engineering based on Information Geometry. This multivariate modeling co-pilot can handle a wider range of time series use cases with award-winning results and incredible performance.

During this demo session we will showcase how best-in-class and very transparent time series models can be built with just one iteration through the data. We will cover several concrete use cases for advanced time series forecasting, anomaly detection and root cause analysis.

Associate Professor | Senior Research Fellow, Biomedical Data Science and Health InformaticsUniversity of Oxford

Sara Khalid

Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.

Me, my Health, and AI: Applications in Medical Diagnostics and Prognostics(Talk)


Why Attend

Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models, and advanced trends

Expand your network and connect with like- minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career

Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning

Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more

More Reasons To Attend?

Download the why attend guide

Who should attend

  • Top speakers and practitioners in Machine Learning and Deep Learning

  • Data Scientists and Data Analysts

  • Decision makers

  • Industry leaders

  • Data Science Enthusiasts

  • Software Developers focused on Machine Learning and Deep Learning

  • Data Science Innovators

  • CEOs, CTOs, CIOs

  • Core contributors in the fields of Machine Learning and Deep Learning

ODSC EUROPE Hybrid Conference 2024

Register your interest for 2024

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