ODSC EUROPE 2023 | IN-PERSON & VIRTUAL CONFERENCE
AI EXPO & DEMO HALL
AI Solutions Showcase | Networking
Tobacco Dock, London – June 15th-16th, 2022
TALKS & DEMOS
AI Solution Providers
Networking Events
Attendees
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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
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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
Pprevious Expo Speakers
Romain Chapman
Romain is one of Appen’s Senior Manager’s, overseeing and supporting their European client-base with Appen’s breadth of Data for the AI Lifecycle services (data sourcing, annotation/labelling, and model evaluation). Romain came from the localization industry and noted firsthand the advancements and impacts of ML/AI via Machine Translation/Transcription, ASR and TTS offerings. Therefore, he saw a transition into the world of AI/ML as the next logical step. Passionate about ethically sourced, high-quality labelled data, which powers Machine Learning/AI programmes for good.
Aoife Cahill, PhD
Aoife Cahill is a Natural Language Processing (NLP) expert and a director of AI research at Dataminr, the leading real-time information discovery platform. Since joining in 2021, Aoife has led a team of data scientists focused on the efficient iterative process of developing and evaluating AI technology that supports the expansion of Dataminr’s internal and external products.
Prior to Dataminr, Aoife led a team of research scientists and engineers working on high-stakes NLP applications in the educational domain at the Educational Testing Service (ETS). The NLP teams at ETS are known leaders in the field of developing and deploying robust, well-documented, scalable NLP prototypes that maintain fairness across user groups.
Aoife holds a PhD in Computational Linguistics from Dublin City University, Ireland, and has also spent time conducting NLP research in Germany, Norway and in the U.S. As an active member of the computational linguistics research community, her research has been published in top-tier journals including Computational Linguistics and the Journal of Research on Language and Computation, as well as conference proceedings at the annual conference for the Association for Computational Linguistics (ACL), the International Conference on Computational Linguistics (COLING) and the Conference on Empirical Methods in Natural Language Processing (EMNLP).
AI for Emergency Response(Demo Talk)
Jake Bengtson
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
Forecasting Crypto Currency Prices with Cloudera Applied Machine Learning Prototypes(Demo Talk)
Glen Ford
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
The Hidden Layers of Tech Behind Successful Data Labeling(Demo Talk)
Robert Magno
Rob Magno is a Sales Engineer/Solution Architect at Run:AI based in New Jersey. He has been working in the Docker and Kubernetes space for the past five years. He enjoys tackling the diverse customer challenges that come with orchestrating AI/ML workloads through Kubernetes.
Building the Best AI Infrastructure Stack to Accelerate Your Data Science(Demo Talk)
Lee Baker
Lee is the General Secretary for the AI Infrastructure Alliance. Based out of the UK, he is responsible for crafting and nurturing relationships with companies to build a canonical stack for AI and ML. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England’s South Coast.
The Rapid Evolution of the Canonical Stack for Machine Learning(Demo Talk)
Kyriakos Fistos
Kyriakos is a Data Scientist and works across a broad set of industries contributing to large scale digital transformation projects for the world’s largest organizations. He specializes in data visualization, predictive modelling and model management.
With his academic background in Mathematics and Operational Research, Applied Statistics and Financial Risk, his day-to-day includes helping clients make better business decisions through Data Management, Business Intelligence, Machine Learning and Artificial Intelligence solutions.
Optimizing Your Analytics Life Cycle with Machine Learning and Open Source(Demo Talk)
Gilad Shaham
Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University.
It worked on my Laptop, now what? Using OS tool MLRun to Automate the Path to Production(Demo Talk)
Oryan Omer
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
Data-driven ML Retraining with Production Insights(Demo Talk)
Joe Depeau
Originally from the USA but now living in the UK, Joe Depeau has over 25 years of varied experience in the IT industry across a number of domains and specialties. For the last 10 years Joe has focused on technical pre-sales and solution architecture in the data and analytics space, and he is a passionate evangelist for the use of graph databases and especially graph data science. When not geeking out over data and technology he enjoys camping, hiking with his dog, reading, tending to his Animal Crossing island, and playing boardgames and RPGs. He also bakes a mean cheesecake.
A Graph Data Science Framework for the Enterprise (Demo Talk)
Allan Stevenson
Allan’s background covers a broad technology stack in infrastructure and cloud, working across a variety of roles in large enterprises before moving into Data Science and ML in recent years. His last role was working on time series forecasting at a fintech scale-up before joining Weights and Biases as the first member of the Customer Success team in EMEA.
Best Practices of Effective ML Teams(Demo Talk)
Terry McCann
Terry is the Director of AI for Advancing Analytics and Microsoft Artificial Intelligence MVP with a focus on all things AI and Data Science. Terry has a passion for applying traditional Software Engineering techniques to Data, to improve the way teams deliver Machine Learning projects. Terry is the host of the popular podcasts Data Science in Production and Totally Skewed, and organises the Global AI Bootcamp London event.
Simplifying Model Production with MLFlow Pipelines and Delta(Demo Talk)
Europe 2022 AI Expo Hall Schedule
We are delighted to announce our Europe 2022 Preliminary Schedule! More sessions coming soon!
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
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).
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
Allan’s background covers a broad technology stack in infrastructure and cloud, working across a variety of roles in large enterprises before moving into Data Science and ML in recent years. His last role was working on time series forecasting at a fintech scale-up before joining Weights and Biases as the first member of the Customer Success team in EMEA.
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
Doris Zhong is a Product Manager in Azure AI Platform organization at Microsoft, and she is focusing on the area of machine learning in hybrid cloud. She loves to communicate with customer to get deep insights, and help solve the real problem. In her early career, she worked on building Microsoft internal GPU training platform, that managed tens of thousands of GPUs, and served thousands of users.
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
Akram Dweikat is a computer engineer and entrepreneur, specialized in machine learning & AI. He has been recognized by the UK Government as an Exceptional Talent in computer engineering, innovation, and entrepreneurship. Akram is currently the Engineering Manager for Deliveroo’s Network Economics (ML) team. Also, he is a global data science ambassador for Z by HP. He has been appointed as an AI Expert by the World Economic Forum, serving on their Global Future Council on Artificial Intelligence for Humanity. In his spare time, Akram helps build agricultural gardens for income and food security in his native Palestine. Earlier in his career, Akram helped establish the entrepreneurial community in Nablus and was one of eight youth selected to meet US President Barack Obama on his official visit to Palestine.
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
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
Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University.
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
I am a senior Pre-sales engineer responsible for Neo4j’s Graph Data Science product across EMEA and APAC. I am also a Machine Learning practitioner – TensorFlow Developer certified – with a deep interest in Graph Neural Networks and pursuing a PhD in this field (part-time), you can check out some of my work on Medium: https://kristof-neys-58246.medium.com/
Delivering value to clients as a trusted advisor is what excites me, and I have a 15y+ proven track record in this area.
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
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
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
Akram Dweikat is a computer engineer and entrepreneur, specialized in machine learning & AI. He has been recognized by the UK Government as an Exceptional Talent in computer engineering, innovation, and entrepreneurship. Akram is currently the Engineering Manager for Deliveroo’s Network Economics (ML) team. Also, he is a global data science ambassador for Z by HP. He has been appointed as an AI Expert by the World Economic Forum, serving on their Global Future Council on Artificial Intelligence for Humanity. In his spare time, Akram helps build agricultural gardens for income and food security in his native Palestine. Earlier in his career, Akram helped establish the entrepreneurial community in Nablus and was one of eight youth selected to meet US President Barack Obama on his official visit to Palestine.
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
Rob Magno is a Sales Engineer/Solution Architect at Run:AI based in New Jersey. He has been working in the Docker and Kubernetes space for the past five years. He enjoys tackling the diverse customer challenges that come with orchestrating AI/ML workloads through Kubernetes.
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
Andy has 20+ years experience in customer success, consulting, training & coaching for martech, edtech and AI businesses. Commercially focussed & high achiever in client retention, expansion & satisfaction metrics. Builder & leader of high performing CS teams, mentor and advisor on strategic growth tactics. Customer experience first attitude to products, services and interactions. Creative & entrepreneurial with a passion for serving others. Driven to deliver for customers, stakeholders and execs with a consistent focus on providing value.
Ulrik Stig Hansen is the co-founder and CEO of Encord, a London-based computer vision training data platform. The company’s platform is used by businesses to make unstructured data readable by machines. Its tools include data annotation, evaluation, and management of training data.
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
Lee is the General Secretary for the AI Infrastructure Alliance. Based out of the UK, he is responsible for crafting and nurturing relationships with companies to build a canonical stack for AI and ML. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England’s South Coast.
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
Pulkit is an Assistant Professor in the department of Electrical Engineering and Computer Science (EECS) at MIT. His lab is a part of the Computer Science and Artificial Intelligence Lab (CSAIL), is affiliated with the Laboratory for Information and Decision Systems (LIDS) and involved with NSF AI Institute for Artificial Intelligence and Fundamental Interactions ( IAIFI ).
He completed Ph.D. at UC Berkeley; undergraduate studies from IIT Kanpur. Co-founded SafelyYou Inc. that builds fall prevention technology.
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
Romain is one of Appen’s Senior Manager’s, overseeing and supporting their European client-base with Appen’s breadth of Data for the AI Lifecycle services (data sourcing, annotation/labelling, and model evaluation). Romain came from the localization industry and noted firsthand the advancements and impacts of ML/AI via Machine Translation/Transcription, ASR and TTS offerings. Therefore, he saw a transition into the world of AI/ML as the next logical step. Passionate about ethically sourced, high-quality labelled data, which powers Machine Learning/AI programmes for good.
Dimitris is a Machine Learning engineer at Mindtech, implementing AI powered computer vision, trained on synthetic data created by their Chameleon platform. Dimitris has held a passion for AI since experiencing the fundamental breakthroughs of the early 2010’s in deep learning as an undergraduate. To pursue this interest, he undertook postgraduate studies in computer vision at Oregon State University. Aware of the limitations of real datasets he became very interested in the potential of replicating real modalities abundantly in a controlled and ethically considerate environment and joined Mindtech to substantiate the use of synthetic data by the next generation of AI.
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
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
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
Kyriakos is a Data Scientist and works across a broad set of industries contributing to large scale digital transformation projects for the world’s largest organizations. He specializes in data visualization, predictive modelling and model management.
With his academic background in Mathematics and Operational Research, Applied Statistics and Financial Risk, his day-to-day includes helping clients make better business decisions through Data Management, Business Intelligence, Machine Learning and Artificial Intelligence solutions.
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
Terry is the Director of AI for Advancing Analytics and Microsoft Artificial Intelligence MVP with a focus on all things AI and Data Science. Terry has a passion for applying traditional Software Engineering techniques to Data, to improve the way teams deliver Machine Learning projects. Terry is the host of the popular podcasts Data Science in Production and Totally Skewed, and organises the Global AI Bootcamp London event.
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
Aoife Cahill is a Natural Language Processing (NLP) expert and a director of AI research at Dataminr, the leading real-time information discovery platform. Since joining in 2021, Aoife has led a team of data scientists focused on the efficient iterative process of developing and evaluating AI technology that supports the expansion of Dataminr’s internal and external products.
Prior to Dataminr, Aoife led a team of research scientists and engineers working on high-stakes NLP applications in the educational domain at the Educational Testing Service (ETS). The NLP teams at ETS are known leaders in the field of developing and deploying robust, well-documented, scalable NLP prototypes that maintain fairness across user groups.
Aoife holds a PhD in Computational Linguistics from Dublin City University, Ireland, and has also spent time conducting NLP research in Germany, Norway and in the U.S. As an active member of the computational linguistics research community, her research has been published in top-tier journals including Computational Linguistics and the Journal of Research on Language and Computation, as well as conference proceedings at the annual conference for the Association for Computational Linguistics (ACL), the International Conference on Computational Linguistics (COLING) and the Conference on Empirical Methods in Natural Language Processing (EMNLP).
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
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
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
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).
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
Allan’s background covers a broad technology stack in infrastructure and cloud, working across a variety of roles in large enterprises before moving into Data Science and ML in recent years. His last role was working on time series forecasting at a fintech scale-up before joining Weights and Biases as the first member of the Customer Success team in EMEA.
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
Doris Zhong is a Product Manager in Azure AI Platform organization at Microsoft, and she is focusing on the area of machine learning in hybrid cloud. She loves to communicate with customer to get deep insights, and help solve the real problem. In her early career, she worked on building Microsoft internal GPU training platform, that managed tens of thousands of GPUs, and served thousands of users.
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
Akram Dweikat is a computer engineer and entrepreneur, specialized in machine learning & AI. He has been recognized by the UK Government as an Exceptional Talent in computer engineering, innovation, and entrepreneurship. Akram is currently the Engineering Manager for Deliveroo’s Network Economics (ML) team. Also, he is a global data science ambassador for Z by HP. He has been appointed as an AI Expert by the World Economic Forum, serving on their Global Future Council on Artificial Intelligence for Humanity. In his spare time, Akram helps build agricultural gardens for income and food security in his native Palestine. Earlier in his career, Akram helped establish the entrepreneurial community in Nablus and was one of eight youth selected to meet US President Barack Obama on his official visit to Palestine.
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
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
Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University.
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
I am a senior Pre-sales engineer responsible for Neo4j’s Graph Data Science product across EMEA and APAC. I am also a Machine Learning practitioner – TensorFlow Developer certified – with a deep interest in Graph Neural Networks and pursuing a PhD in this field (part-time), you can check out some of my work on Medium: https://kristof-neys-58246.medium.com/
Delivering value to clients as a trusted advisor is what excites me, and I have a 15y+ proven track record in this area.
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
Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.
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
Akram Dweikat is a computer engineer and entrepreneur, specialized in machine learning & AI. He has been recognized by the UK Government as an Exceptional Talent in computer engineering, innovation, and entrepreneurship. Akram is currently the Engineering Manager for Deliveroo’s Network Economics (ML) team. Also, he is a global data science ambassador for Z by HP. He has been appointed as an AI Expert by the World Economic Forum, serving on their Global Future Council on Artificial Intelligence for Humanity. In his spare time, Akram helps build agricultural gardens for income and food security in his native Palestine. Earlier in his career, Akram helped establish the entrepreneurial community in Nablus and was one of eight youth selected to meet US President Barack Obama on his official visit to Palestine.
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
Rob Magno is a Sales Engineer/Solution Architect at Run:AI based in New Jersey. He has been working in the Docker and Kubernetes space for the past five years. He enjoys tackling the diverse customer challenges that come with orchestrating AI/ML workloads through Kubernetes.
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
Andy has 20+ years experience in customer success, consulting, training & coaching for martech, edtech and AI businesses. Commercially focussed & high achiever in client retention, expansion & satisfaction metrics. Builder & leader of high performing CS teams, mentor and advisor on strategic growth tactics. Customer experience first attitude to products, services and interactions. Creative & entrepreneurial with a passion for serving others. Driven to deliver for customers, stakeholders and execs with a consistent focus on providing value.
Ulrik Stig Hansen is the co-founder and CEO of Encord, a London-based computer vision training data platform. The company’s platform is used by businesses to make unstructured data readable by machines. Its tools include data annotation, evaluation, and management of training data.
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
Lee is the General Secretary for the AI Infrastructure Alliance. Based out of the UK, he is responsible for crafting and nurturing relationships with companies to build a canonical stack for AI and ML. When not shuttling his 3 children around, he can most often be found cycling, running and swimming around England’s South Coast.
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
Pulkit is an Assistant Professor in the department of Electrical Engineering and Computer Science (EECS) at MIT. His lab is a part of the Computer Science and Artificial Intelligence Lab (CSAIL), is affiliated with the Laboratory for Information and Decision Systems (LIDS) and involved with NSF AI Institute for Artificial Intelligence and Fundamental Interactions ( IAIFI ).
He completed Ph.D. at UC Berkeley; undergraduate studies from IIT Kanpur. Co-founded SafelyYou Inc. that builds fall prevention technology.
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
Romain is one of Appen’s Senior Manager’s, overseeing and supporting their European client-base with Appen’s breadth of Data for the AI Lifecycle services (data sourcing, annotation/labelling, and model evaluation). Romain came from the localization industry and noted firsthand the advancements and impacts of ML/AI via Machine Translation/Transcription, ASR and TTS offerings. Therefore, he saw a transition into the world of AI/ML as the next logical step. Passionate about ethically sourced, high-quality labelled data, which powers Machine Learning/AI programmes for good.
Dimitris is a Machine Learning engineer at Mindtech, implementing AI powered computer vision, trained on synthetic data created by their Chameleon platform. Dimitris has held a passion for AI since experiencing the fundamental breakthroughs of the early 2010’s in deep learning as an undergraduate. To pursue this interest, he undertook postgraduate studies in computer vision at Oregon State University. Aware of the limitations of real datasets he became very interested in the potential of replicating real modalities abundantly in a controlled and ethically considerate environment and joined Mindtech to substantiate the use of synthetic data by the next generation of AI.
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
Glen Ford is VP of Product at iMerit — a leading AI data solutions company — where he leads the product management and design teams. Glen holds more than two decades of product development experience across the technology sector. A Graduate of Texas A&M University—Commerce, Glen began his career as a consultant where he handled full-stack web programming and architecture for clients including Time Warner and AIM Funds. Over the years, he has held senior and director-level product management roles at several companies including Demand Media, WP Engine and Humanify. Most recently, Glen spent four years at Alegion — an ML-powered data annotation platform — where he helped the company grow from eight full-time employees to more than 100 in a challenging, emerging market.
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
Kyriakos is a Data Scientist and works across a broad set of industries contributing to large scale digital transformation projects for the world’s largest organizations. He specializes in data visualization, predictive modelling and model management.
With his academic background in Mathematics and Operational Research, Applied Statistics and Financial Risk, his day-to-day includes helping clients make better business decisions through Data Management, Business Intelligence, Machine Learning and Artificial Intelligence solutions.
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
Terry is the Director of AI for Advancing Analytics and Microsoft Artificial Intelligence MVP with a focus on all things AI and Data Science. Terry has a passion for applying traditional Software Engineering techniques to Data, to improve the way teams deliver Machine Learning projects. Terry is the host of the popular podcasts Data Science in Production and Totally Skewed, and organises the Global AI Bootcamp London event.
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
Aoife Cahill is a Natural Language Processing (NLP) expert and a director of AI research at Dataminr, the leading real-time information discovery platform. Since joining in 2021, Aoife has led a team of data scientists focused on the efficient iterative process of developing and evaluating AI technology that supports the expansion of Dataminr’s internal and external products.
Prior to Dataminr, Aoife led a team of research scientists and engineers working on high-stakes NLP applications in the educational domain at the Educational Testing Service (ETS). The NLP teams at ETS are known leaders in the field of developing and deploying robust, well-documented, scalable NLP prototypes that maintain fairness across user groups.
Aoife holds a PhD in Computational Linguistics from Dublin City University, Ireland, and has also spent time conducting NLP research in Germany, Norway and in the U.S. As an active member of the computational linguistics research community, her research has been published in top-tier journals including Computational Linguistics and the Journal of Research on Language and Computation, as well as conference proceedings at the annual conference for the Association for Computational Linguistics (ACL), the International Conference on Computational Linguistics (COLING) and the Conference on Empirical Methods in Natural Language Processing (EMNLP).
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
Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.
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