The biggest European conference about ML, AI and Deep Learning applications
running in person in Prague and online.
Machine Learning Prague 2024
In cooperation with Kiwi.com
– , 2024Registration
World class expertise and practical content packed in 3 days!
You can look forward to an excellent lineup of 40 international experts in ML and AI business and academic applications at ML Prague 2024. They will present advanced practical talks, hands-on workshops and other forms of interactive content to you.
What to expect
- 1000+ Attendees
- 3 Days
- 40 Speakers
- 10 Workshops
- 1 Party
Murray CampbellDistinguished Research Scientist, IBM T. J. Watson Research Center
Murray Campbell is a Distinguished Research Scientist in artificial intelligence at the IBM T. J. Watson Research Center. His current research interests include the evaluation of AI systems and neuroscience-inspired AI architectures. He received his B.Sc. and M.Sc. in computing science from the University of Alberta, and his Ph.D. in computer science from Carnegie Mellon University. He was a member of the IBM team that developed Deep Blue, which was the first computer to defeat the human world chess champion in a match. He received numerous awards for Deep Blue, including the Allen Newell Medal for Research Excellence and the Fredkin Prize. He is an ACM Distinguished Scientist and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).
Amey DharwadkerMachine Learning Engineering Manager, Meta
Amey Porobo Dharwadker works as a Machine Learning Engineering Manager at Meta, leading the Facebook Video Recommendations Ranking team. Renowned for his pivotal role in developing personalization models that have significantly contributed to Facebook's impressive user growth, his work has powered the success of Facebook Watch and Reels, engaging over 1.25 billion monthly users. His career at Facebook includes substantial strides in enhancing user engagement and revenue growth through his work in News Feed and Ads Machine Learning. As a prolific researcher, he has published several international publications in recommender systems. He shares his insights through prominent tech publications and actively contributes to the AI community by serving on program committees for prestigious venues including NASA Frontier Development Lab USA, IJCAI, AAAI, CIKM and WebConf. Amey is a thought leader and sought-after speaker at esteemed AI conferences, while also lending his expertise to hackathons, angel syndicates and startup accelerators as a mentor. He holds a Master's degree from Columbia University, USA and a Bachelor's degree from the National Institute of Technology Tiruchirappalli, India.
Lucie FlekFull professor, University of Bonn
Lucie Flek is a full professor at the University of Bonn, leading the Data Science and Language Technologies group. Her main interests lie in machine learning research for natural language processing (NLP), including AI robustness and safety. The application areas range from large language models and conversational systems, across clinical NLP and mental health research, to misinformation detection and social media analyses. Prof. Flek has been active both in academia and industry – she used to manage natural language understanding research programs in Amazon Alexa and contributed to the Google Shopping Search launch in Europe. Her academic work at the University of Pennsylvania and University College London revolved around user modeling from text, and its applications in psychology and social sciences. Her PhD at TU Darmstadt focused on meaning ambiguity, incorporating expert lexical-semantic resources into DNN classification tasks. She has served as Area Chair for Computational Social Sciences at numerous ACL* conferences, and as an editor of the NLP section of multiple AI journals. Before her career path in natural language processing, Prof. Flek has been contributing to particle physics research at CERN in the area of axion searches.
Hamed ValizadeganMachine Learning Lead, NASA
Holder a PhD in computer science with a focus in Machine Learning, Dr. Valizadegan has more than 20 years of experience in Artificial Intelligence and Machine Learning. He is a data science manager and machine learning lead at USRA and a senior machine learning scientist at NASA through NASA Academic Mission Services (NAMS). Dr. Valizadegan has applied machine learning expertise in a diverse set of domains from engineering and biology to medicine and astronomy. At NASA, Dr. Valizadegan has been involved in several machine learning projects related to Hubble Space Telescope, Kepler and TESS missions, James Web Space Telescope (JWST) mission, Space Biology, and Orion Vehicle. Dr. Valizadegan has led a team of scientists who developed machine learning models to discover 370 new exoplanets to date.
Stefan JosefSenior Data Scientist, Dataclair
Stefan is a Senior Data Scientist at Dataclair, AI centre at O2 Czech Republic, since 2021, where he has been leading research efforts in applying advanced NLP and Deep Learning techniques to geospatial data. Drawing from his experience with language models and transfer learning since 2018, Stefan adeptly devises innovative solutions for diverse datasets and complex business problems. Stefan holds a M.Sc. in Economics from Stockholm University, where he specialized in empirical macroeconomics. Before shifting his focus on AI, he was working as project and product manager.
Martin PlajnerDirector of the Research and Development department, Logio
Martin Plajner is the Director of the Research and Development department in the consultancy company Logio. This department keeps the company at the technological edge by seeking novel approaches, prototyping, and defining new products. An inseparable part of the R&D team are trainees; students, who represent the company's future. Consequently, he preserves his link to academia as a researcher at the Czech Academy of Sciences - Institute of Information Theory and Automation (UTIA). These two parts provide an opportunity to combine the business and the academic world and to challenge both theoretical concepts as well as established practices.
Ludek KopacekData Science & ML Manager, Workday
Ludek Kopacek is a Data Science & ML Manager at Workday. Ludek leads the team focused on enhancing augmented analytics' ability to identify smart insight by integrating domain-specific Large Language models (LLM). His former experience comprises pricing optimization algorithms and ML-based sales optimization algorithms at Vendavo. He is passionate about staying up-to-date with the latest trends in ML and LLM. Not only because he is a fan of new developments, but he also brings experience in leveraging data science to drive business outcomes.
Daniel CosteaSenior Software Engineer, European Agency
Daniel Costea is a senior software developer with more than two decades of experience and a passion for C# since its inception in 2001. He has been a frequent international speaker at over 50 conferences across Europe and the US in the past 6 years, and has organized community meetings and workshops. As a trainer, Daniel has delivered over 1200 hours of training in .NET web technologies to more than 700 students. He is also a published author of articles on .NET technologies, and was recognized as a Microsoft MVP in developer technologies in 2019.
Tun ShweVP of Data, Quix
Tun Shwe is the VP of Data at Quix, where he leads data strategy and developer relations. He is focused on helping companies imagine and execute their strategic data vision with stream processing at the forefront. He was previously a Head of Data and Data Engineer at high growth startups and has spent his career leading T-shaped teams in developing analytics platforms and data-intensive AI applications. In his spare time, Tun goes surfing, plays guitar and tends to his analogue cameras.
Tomáš NeubauerCTO and co-founder, Quix
Tomas Neubauer is a co-founder and CTO at Quix, where he works as the technical authority for the engineering team and is responsible for the direction of the company across the full technical stack. He was previously technical lead at McLaren, where he led the architectural uplift of the real-time telemetry acquisition platform for the Formula 1 racing team.
Petr SimecekResearcher, Mediaboard
Biostatistician (by training) turned into time series forecaster at Google. Currently untangling knots at protein backbones at Masaryk University and training llamas and other LLM creatures at Mediaboard (ex-Monitora a.s.). When not coding in R & Python, I'm sharing tips with over 40K @python_tips followers and trying to keep the ML community buzzing in (ML meetups Brno, PyLadies data course in Prague). Just a tech geek at heart and a big believer in AI's promise.
David JiříčekSenior Python Developer, Mediaboard
Over the past three years, David has been a pivotal member of Mediaboard, a leading Czech media monitoring company. He has been instrumental in spearheading innovative projects, focusing primarily on Natural Language Processing. His work involves developing cutting-edge techniques for data extraction, text summarization and clustering, significantly enhancing the company's media analysis capabilities. He holds a degree in physics and mathematical modeling from Charles University in Prague.
Idrees MuhammadChief Technology Officer & Co-Founder, Turing Artificial Intelligence
Idrees is a computational scientist with a degree in Mechatronics Engineering. Having 5 years of experience in research and development in simulation domain, he recently started his new research venture named Turing-Lab for developing ML based numerical solutions. At Turing Lab, he is researching on developing and implementing scalable Machine learning based framework for solving complex problems in computational science ranging from design space exploration to inverse physics problems. He is a C++ nerd and a strong advocate of FOSS who loves building open source tools in his free time.
Marina VolkovaData Scientist, HumbleBuildings
I’ve been working over a decade in the field of Machine Learning, tackling challenges in areas such as medical diagnosis, cybersecurity, and various industries. Presently, I hold the position of Data Scientist at Humble Buildings, a Dutch company dedicated to technical monitoring of buildings. My current project revolves around exploring the capabilities of Large Language Models (LLMs). The primary objective is to revolutionize how our users, many of whom are non-technical individuals, interact with information. Throughout my job, I have accumulated some insights and lessons, and I look forward to sharing both the do's and don'ts with the audience.
Lucie BlechovaData Science Team Lead, Kiwi.com
Lucie currently leads one of the Data Science teams in Kiwi.com that specializes in customer-centric data science solutions. Lucie's professional experience includes Data Science and ML Engineering positions across multiple industries such as health care, pharmaceuticals, commodity trading, the energy industry, and of course the exciting travel industry that Kiwi.com is a significant part of. As far apart as these fields might seem, data science and data in general is what connects them all.
Thomas BrowneAI Team Lead, Kiwi.com
AI team lead at Kiwi.com where he focuses on mathematical theory to address travel search-related problems with machine learning. In the past he graduated from Paris Cité University, France, with a PhD in probability and statistics for numerical simulators. He also has experience with applying machine learning in the fields of energy - reliability in nuclear plants - and pharmaceutical industry - identification of key features in cancer drug development. On a much lighter note, he is a huge fan of indie/punk music and loves cooking.
Oliver EignerResearcher, St. Pölten University of Applied Sciences
Oliver Eigner is a passionate researcher at the St. Pölten University of Applied Sciences in the center for artificial intelligence in Austria, who is keen on trustworthy AI, human-centered AI as well as large language models. He finished his master’s degree with distinction in Information Security at the St. Pölten University of Applied Sciences in 2016. He is also an enthusiastic lecturer of Trustworthy AI and Python programming language.
Fabian KovacResearch Assistant, St. Pölten University of Applied Sciences
Fabian Kovac is a 30-year-old Research Assistant at the St. Pölten University of Applied Sciences in Austria with a strong focus on Reinforcement Learning as well as sensor data and time series analysis. Before finishing his bachelor’s degree with distinction in Data Science & Business Analytics at the St. Pölten University of Applied Sciences in 2022, he had a background in software engineering, where he also the honor to lead the backend team at the time. After finishing his master’s degree, Fabian is pursuing a PhD to further work towards his dream to leave his footprints in the world.
Jan RusResearch Team Lead, Emplifi
Jan Rus is a Research Team Lead at Emplifi.io, a unified CX platform for enterprises. Jan manages a team of researchers mostly responsible for the design, research, and development of core product features exploiting big data analysis and machine learning techniques. Creation of concepts and bringing them from concepts to working prototypes and implementations. Lately, the team has been working on problems such as sentiment analysis, NSFW image detection and classification, LLM-based content creation assistant, or retrieval augmented generation for product help and support.
Peter KrejzlDirector of Research, Emplifi
Peter Krejzl is a Director of Research at Emplifi.io, a unified CX platform for enterprises. Peter manages the international research team working on a wide variety of machine learning solutions, from natural language processing to image analysis, anomaly detection, or conversational AI. The team helps apply machine learning to solve tasks across the whole unified CX platform including marketing, care, and commerce solutions. Peter’s main point of interest is natural language processing and applications of AI in various marketing and care tasks, with a recent focus on generative AI.
Massimiliano KuckData Science Consultant, Sopra Steria SE
With an engineering background in Production Automation and IT, Massimiliano embarked on a career as a Data Science Consultant at Sopra Steria. He has several years of experience in implementing AI systems, focusing on the operationalization of Machine Learning models and the interpretability of Black Box algorithms. Massimiliano leverages his expertise to advance the field of Data Science, ensuring that complex models are both deployable and understandable, thereby enhancing the decision-making processes within Sopra Steria’s client projects in in the telecom sector.
Michele KorosecData Science Consultant, Sopra Steria SE
After obtaining a Master's degree in physics from the Technical University in Munich, Michele decided to leave academia behind and join Sopra Steria as a Data Science Consultant in 2022. She has an in-depth knowledge about graph databases, with a specialization on telecommunication networks. She applies this knowledge in her everyday project work at Sopra Steria for one of the major network providers in Germany for whom they build a digital twin of the mobile network based on a graph database. In alignment with her background as a physicist, Michele is also responsible for coordinating quantum computing projects in the German telecom sector of Sopra Steria.
Would you like to become a speaker and talk to 1,000+ attendees? Apply using this form before January 15th, 2024.
Practical & Inspiring Program
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (workshops won't be streamed)
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Using Graph Neural Networks to improve telecommunication networks
Michele Korosec, Sopra Steria SE
This workshop aims to provide an in-depth understanding of modeling and optimizing telecommunication networks using Graph Neural Networks (GNNs). Telecommunication networks characterized by a complex structure of interconnected nodes can be naturally modelled as graph data structures. However the utilization of graph data structures in Machine Learning poses unique challenges. The workshop focuses on various aspects of integrating GNNs into the telecommunication networks to enhance their performance and customer satisfaction. The implementation of GNNs introduces a new level of sophistication that allows intelligent decision making effective network mapping prediction and optimization. The key component of the workshop is a practical use case where participants will work with a Neo4J graph database. This database aids in visualizing and analyzing the complex connections in the network to provide a base for the GNN. With the use of GNNs valuable insights and predictions on network activity can be made efficiently. They leverage the rich information present in the network structure. Using these predictions to optimize network configuration settings and address network problems could potentially result in lower operational costs improved network efficiency and higher customer satisfaction. The workshop aims for participants to learn the practical application of modeling telco networks as graph structures implementing GNNs and interpreting their predictions for optimization. Moreover it's noteworthy that graph structures are not confined to telecommunications they can be found spanning across any industry sector and GNNs have versatile applicability in different use cases. It is an excellent opportunity for practitioners researchers and anyone interested in graph data modeling and machine learning to learn from industry experts. By the end of this workshop participants will have a more profound understanding of the potential of GNNs enabling them to apply this technology to optimize various network systems.
Automated Evaluation of LLM based systems
Marek Matiáš, O2/ Dataclair
Development of complex LLM based solutions cannot be done without robust system for evaluation of the outputs. Whenever you make changes to models prompts or other components of the system you need a metric to find out whether the performance improved overall. We will outline possible solution to this problem including broader picture as well as hands-on exercise.
Chatting with your Data: A Hands-on Introduction to LangChain.
Marina Volkova, HumbleBuildings
In this interactive workshop we are covering how to handle models to work with your own data whether it's text PDFs CSVs or SQL database. We'll show you how to efficiently run Language Models (LLMs) on a standard 16GB RAM machine. During the workshop you will gain hands-on experience with the LangChain framework and have the opportunity to create an application that allows you to interact with your data through natural language. The beauty of this workshop lies in its accessibility as we leverage low-code frameworks that make it suitable for a broad audience. All you need is a laptop with an internet connection access to the Google Colab environment and optionally an OpenAI API key to achieve even more impressive results. Join us on this exciting journey of data interaction and exploration!
Empowering Question Answering Systems with RAG: A Deep Dive into Retrieval Augmented Generation
Peter Krejzl, Emplifi
Join us for a workshop on Retrieval Augmented Generation (RAG) a groundbreaking approach in machine learning that seamlessly integrates information retrieval with text generation. Dive deep into the construction of advanced question-answering systems that leverage private knowledge bases moving beyond mere document retrieval to generate coherent and natural language responses. Throughout the workshop participants will benefit from hands-on experiences utilizing real datasets and the latest LLM techniques ensuring practical comprehension. By harnessing the power of semantic search and private databases RAG-based system promises a new user experience and proprietary context-specific solutions. - Introduction to RAG: We will begin by introducing the concept of Retrieval Augmented Generation (RAG) and its importance in the landscape of modern machine learning solutions. - Dual Strength: RAG uniquely combines an information retrieval component with a text generation model offering a two-pronged approach to problem-solving. - Private Knowledge Bases: Traditional systems rely heavily on public datasets. In this workshop participants will learn to build systems that leverage private knowledge bases ensuring proprietary and context-specific responses. - Beyond Simple Retrievals: It's not just about finding the right documents. Our RAG-based system will not merely retrieve relevant articles but will craft answers in coherent and natural language enhancing user experience. - Practical Implementation: We will guide attendees through the process of building a question-answering system powered by semantic search and the latest LLM (Language Model) techniques. - Hands-on Experience: Participants will get hands-on experience working with real datasets and observing live demonstrations ensuring a practical understanding of the RAG system. - Future Potential: The workshop will conclude with a discussion on the future potential and advancements of RAG in diverse applications. Join us for the workshop and elevate your understanding of how RAG is reshaping the frontier of question-answering systems.
Real-Time Anomaly Detection in Python
Tomáš Neubauer, Quix
Get to grips with real-time anomaly detection in Python by working through a use case for detecting cyclist crashes. In this hands-on workshop we will learn how to build a streaming data pipeline using Kafka to handle telemetry events from a bicycle sensor/fitness app. From here we will collect data to label and train an ML model and deploy it to predict crashes as they happen in real time.
Using Machine Learning for filtering itineraries: A real-life Kiwi.com use case
Thomas Browne, Kiwi.com
In this workshop the participants will get an insight into how we utilize Machine Learning in practice in Kiwi.com. We will introduce a business case about filtering itineraries that we want to display through our travel search partners based on customers’ interest. The motivation is to increase the attractiveness of our offer. This session will be an opportunity for the attendees to follow the whole life-cycle of a Kiwi.com ML project - from the original business problems to the final deployment of our solution taking into account both data science and engineering-related issues. We will start by defining expected goals and wonder how this translates into a ML problem. The latter will involve proper metrics definition (what does an attractive itinerary actually mean in terms of data?) model design (ML classification on whether a given itinerary is attractive) and evaluation approach that is used to guarantee the solution’s added value. We will follow by introducing the dataset on our itineraries and we will go through the featurisation process together. Once features are created we will move towards the model build and selection phase where a comparison of multiple approaches will be provided - a decisive evaluation will help us make the best decision. Afterwards we will define our filtering methodology - throughout simulations on historical data we will estimate the optimal threshold on our classifier. The goal will be to maximise the interest dragged by the itineraries that we decide to display. Just like our other regular projects the attendees will then be taken through the ABtest phase and what is needed to adopt our developed solution. At last we will share the requirements to deploy the solution and give an insight on the frequency of re-training the ML model.
Finetuning Open-Source LLMs to small languages
Petr Simecek, Mediaboard
Large Language Models (LLMs) represent a remarkable advancement in artificial intelligence (AI) boasting the capability to generate and comprehend human language. Derived from extensive training on vast text and code datasets these models excel in a variety of tasks such as translation summarization and question answering. However a major limitation arises when these LLMs predominantly trained on English data are applied to other languages particularly smaller languages like Czech. Notable models like ChatGPT Bard and Claude exhibit proficiency in Czech with minimal grammatical and stylistic errors. Yet many contemporary open-source LLMs influenced heavily by English-centric datasets fail to address even basic Czech queries.So what are the choices? At Monitora our initial experiments with ChatGPT for text summarization have now transitioned to Llama2 7B primarily due to privacy considerations. We are also evaluating Mistral models introduced in September. Within this workshop I will give introduction into QLoRA adapters and demonstrate that instruction finetuning of such models is possible even with limited resources (a single consumer GPU). We will see that models originally speaking a very broken language improve significantly by this process. In addition to the technical insights I envision this workshop as a collaborative forum. Rather than just being a traditional presentation it aims to be a platform for knowledge exchange. Attendees are encouraged to contribute their insights plans or experiences related to the application of LLMs in small languages. For structure I would like to limit each interested participant to 5 slides and a presentation time of 5 minutes.
Building OpenAI Plugins: Deep Dive into Microsoft Semantic Kernel (SK)
Daniel Costea, European Agency
Microsoft Semantic Kernel (SK) is a new technology that enables the integration of AI Large Language Models (LLMs) like GPT-3.5-Turbo GPT-4 and DALL-E 3 from OpenAI or Azure OpenAI with conventional programming languages like C# Python and Java. SK brings together several key components to provide planning and execution capabilities. These components include a robust kernel that provides the foundation for all other components plugins (formerly known as skills) for performing specific tasks connectors for interfacing with external systems memories for storing information about past events steps for defining individual actions and pipelines for organizing complex multi-stage plans. In this hands-on workshop we explore how to build various plugins for: - building a semantic interface for an existing API using plugins and execution plans containing semantic and native functions. - building a GPT-powered chat enriched by real-time information and memories enhanced through RAG (Retrieval-Augmented Generation) capabilities. - building a cutting-edge generative model using DALL-E 3 and multi-modal input.
Unlocking the Power of Active Learning: A Hands-on Exploration
Fabian Kovac, St. Pölten University of Applied Sciences
In today's ever-evolving landscape of Artificial Intelligence and Machine Learning staying at the cutting edge is not just an advantage it's a necessity. Active Learning has emerged as a powerful technique that has the potential to revolutionize how we train machine learning models. With this hands-on workshop we are providing attendees with a comprehensive understanding of its relevance benefits and practical applications. Active Learning is a paradigm-shifting approach that focuses on enabling machines to learn more efficiently from limited labeled data by actively selecting the most informative examples for annotation. It plays a pivotal role in various industries and research domains offering solutions to some of the most pressing challenges in AI and machine learning. By actively involving human experts in the loop Active Learning not only reduces annotation costs and efforts but also accelerates model development making it particularly relevant for resource-constrained environments where only limited labeled data is available. This workshop will be a hands-on immersive experience providing a solid foundation of the theoretical underpinnings ensuring attendees grasp the core concepts. Participants will have the opportunity to apply Active Learning techniques to real datasets gain practical experience in selecting informative data points training models and observing the impact on model performance. Furthermore we will share best practices and pitfalls to avoid when implementing Active Learning in real applications. This workshop promises to equip attendees with the knowledge and skills they need to harness the full potential of Active Learning in their research and industry applications. By the end of the workshop participants will be well-prepared to incorporate this cutting-edge technique into their AI and Machine Learning endeavors accelerating progress and achieving superior results.
Power of Physics-ML, a hands-on workshop with open-source tools
Idrees Muhammad, Turing Artificial Intelligence
In this workshop participants will learn how to combine the power of neural networks with the laws of physics to solve complex scientific and engineering problems using open-source tools. Physics-Informed Neural Networks (PINNs) have gained popularity in various fields including fluid dynamics material science and structural engineering for their ability to incorporate physical principles into machine learning models. Attendees will learn how to leverage open-source tools to build and train PINNs enabling them to model and solve complex physical systems efficiently.
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (and on-line)
Registration from 9:00
Program will be announced.
Conference day 1
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (and on-line)
Doors open at 08:30
Program will be announced.
Have a great time Prague, the city that never sleeps
You can feel centuries of history at every corner in this unique capital. We'll invite you to get a taste of our best pivo (that’s beer in Czech) and then bring you back to the present day to party at one of the local clubs all night long!
Venue ML Prague 2024 will run hybrid, in person and online!
The main conference as well as the workshops will be held at O2 Universum.
We will also livestream the talks for all those participants who prefer to attend the conference online. Our platform will allow interaction with speakers and other participants too. Workshops require intensive interaction and won't be streamed.
Českomoravská 2345/17a, 190 00, Praha 9
Českomoravská 2345/17a, 190 00, Praha 9
Now or never Registration
What You Get
- Practical and advanced level talks led by top experts.
- Party in the city with people from around the world. Let’s go wild!
- Delicious food and snacks throughout the conference.
They’re among us We are in The ML Revolution age
Machines can learn. Incredibly fast. Faster than you. They are getting smarter and smarter every single day, changing the world we’re living in, our business and our life. The artificial intelligence revolution is here. Come, learn and make this threat your biggest advantage.
Our Attendees What they say about ML Prague
Are you attending too? Do you have tips for what not to miss?February 27, 2021
Guys, job more than well done 👍 thanks for great conference🙂— Ivan Kasanický (@IvanKasanicky) February 28, 2021
Thank you to Our Partners
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Scientific program & Co-Founder
Gonzalo V. Fernández
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