The biggest European conference about ML, AI and Deep Learning applications
running in person in Prague and online.
Machine Learning Prague 2023
In cooperation with Kiwi.com
– , 2023Registration
World class expertise and practical content packed in 3 days!
You can look forward to an excellent lineup of 45 international experts in ML and AI business and academic applications at ML Prague 2023. They will present advanced practical talks, hands-on workshops and other forms of interactive content to you.
What to expect
- 1000+ Attendees
- 3 Days
- 45 Speakers
- 10 Workshops
- 1 Party
Martin SchmidSenior Research Scientist, DeepMind
CEO & Co-Founder of EquiLibre Technologies. Previously Senior Research Scientist at DeepMind. Co-author of DeepStack and Player of Games.
Marek RosaCEO / Founder, GoodAI
Marek Rosa is the CEO of GoodAI, a general artificial intelligence R&D company, and the CEO of Keen Software House, an independent game development studio best known for its best-seller Space Engineers (4 million copies sold).
Marek founded GoodAI in 2014 with a $10M investment of his own money.
Currently, Marek is occupied with the development of Space Engineers, VRAGE engine, AI Game, and general AI research project Memetic Badger.
Mireia Diez SánchezSenior Researcher, Brno University of Technology
Dr. Mireia Diez Sánchez is a researcher at the Speech@FIT group at Brno University of Technology. Mireia received her Electronic Engineering degree in 2009, and her Ph.D. in 2015, both from the University of the Basque Country, Spain. Her thesis focused on the study of features for Language and Speaker recognition. In 2016 she obtained an individual Marie Curie fellowship for the SpeakerDICE project dealing with diarization tasks. She has attended several international workshops dedicated to the field of speaker recognition and diarization.
Mehrnoosh SamekiPrinciple Product Lead, Responsible AI Tooling, Microsoft
Mehrnoosh Sameki is a principal product lead at Microsoft, responsible for leading the product efforts on machine learning interpretability and fairness within the Open Source and Azure Machine Learning platform. She has cofounded Error Analysis, Fairlearn and Responsible AI Toolbox and has been a contributor to the InterpretML offering. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor, offering courses in responsible AI.
Sai KrishnaGVP of Engineering, SolarWinds
Sai is the GVP of Engineering, leading AIOps, Service Management and Database portfolios for SolarWinds. He is a seasoned Technology & Engineering Leader & Entrepreneur with over 2 decades of experience in scaling & leading global teams, innovating and building winning products in a variety of technologies. He has been leading the charter for AI at SolarWinds and prior to SolarWinds, he was heading Engineering for the IT Solutions portfolio at Atlassian.
Aisling O’SullivanSenior Data Scientist, Dataclair
Aisling O’Sullivan is a Senior Data Scientist at O2 AICentre/Dataclair where she uses machine learning to help accelerate medical research. She has worked with pharmaceutical companies to help discover novel targets for cancer immunotherapy and other applications. She previously received her PhD in Computational Neuroscience from Trinity College Dublin where she used machine learning models to understand how the brain processes speech and language. She also spent time as a visiting PhD researcher at the University of Rochester, USA and received her degree and master’s in Biomedical Engineering from Trinity College Dublin.
Michal MarusanSenior Cloud Solution Architect, Data and AI, Microsoft
Michal Marusan is a senior cloud solution architect at Microsoft, focusing on Data & AI services, responsible for adoption and design of solutions and projects for the enterprise customers starting AI & ML workloads on Azure. He drove adoption of Azure ML and related AI services in various industries - Telco, Banking, Gaming, Retail.
Michal ŠtefánikNLP team lead, Gauss Algorithmic
Michal Štefánik is a senior language specialist in the NLP team at Gauss Algorithmic and a researcher at Masaryk University. Throughout the last six years in NLP, he led the deployment of Attention-based models to numerous NLP applications, ranging from Named Entity Recognition to Machine Translation.
Michal conducts research on enhancing the robustness of large language models, including generalization to unseen tasks. He is also a founder of the students' Transformers Club, whose members received international prizes, including first place in Meta's NAACL DADC competition.
Nikola GroverováNLP data scientist, Gauss Algorithmic
Specialist in natural language processing, graduate of applied mathematics and stochastic methods. Recreational climber.
Thomas BrowneSenior Data Scientist, Kiwi.com
Senior data scientist 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.
Lucie BlechováMachine Learning Engineer, Kiwi.com
ML Engineer at Kiwi.com working on predictive models and their technical implementation in production environment. She has a Master's degree in Economics from Charles University in Prague. She has worked in data teams her entire career, first working for the Ministry of Health and then a pharmaceutical company, afterwards starting full-time at an energy company in Prague and then moving to the Netherlands to work for a commodity trading company. She has experience with delivering data science and machine learning solutions in all those fields. Despite the fact that they might seem unrelated, data is what connects them all. In her personal life, she loves science, sci-fi, martial arts, yoga, hiking, and, during summers, wild swimming.
Martin PlajnerHead of Research and Development, Logio
Martin Plajner is the Director of the Research and Development department in the consultancy company Logio. This department's goal is to keep the company at the technological edge and to provide new methods and methodology. This is done 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 desires to preserve his link to academia and is a junior researcher at the Institute of Information Theory and Automation (UTIA) in the field of decisions making theory with mathematical modeling background from Ph.D. studies and the Czech Technical University. 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.
Theodor PetříkConsultant in Research and Development, Logio
Theodor Petřík is an R&D consultant in the company Logio. He has worked in the company as a trainee over most of his university studies and then naturally became a full-time consultant. Theodor is also a Ph.D. candidate at the Institute of Economic Studies (IES) at the Faculty of Social Sciences, Charles University where he is researching how companies should conduct strategic operations planning to utilize their scarce resources in the most efficient way. The knowledge gained from theoretical research can be applied to real-world applications and the experience obtained from the real-world projects provides in return a unique perspective on theoretical concepts.
Petr ŠimánekSenior Researcher, FIT CTU
Petr is a mathematician and senior researcher at FIT CTU. Petr is involved in many ML projects and is focused on merging ML with known or unknown physics and dynamics.
Kurt KuppensData scientist, Solarwinds
Kurt is a staff data scientist at the Department of Data Science, SolarWinds Czech s. r. o. IT Professional with more than 20 years of IT experience across multiple functions. Data Scientist, Business Programs Manager, IT Architect, Database Management, Leadership. Passionate about Data Science and Big Data. As a Data Scientist his experience includes anomaly detection in time-series data with the use of statistical and machine learning methods, text mining, sentiment analysis, text classification and topic mining. In addition to statistical and machine learning methods he also has experience with deep learning and big data platforms like Elasticsearch.
Venkata PappuPrincipal Machine Learning Engineer, Solarwinds
Venkat is the Principal Machine Learning Engineer at SolarWinds. He is currently working on building ML/AI features for SolarWinds observability solutions. Throughout his 12+ years of career, Venkat has worked across diverse roles within the ML/AI space building solutions for various domains like retail, enterprise collaboration, security, consumer, and consumer electronics, and has vast experience in mentoring and scaling ML teams across established organizations and startups. Prior to joining SolarWinds, Venkat was the Chief Data Scientist at a GRC technology startup.
Jiří PihrtResearcher, FIT CTU
Jirka is a graduate student and researcher at FIT CTU. Currently, he is involved primarily in machine learning for spatiotemporal predictions, but he also has previous experience in augmented/virtual reality and web development.
Jozef ReginacData Science Lead, STRV
Jozef was the first data engineer at STRV and is now leading data science team. Previously long-time data analyst in many fields including forensic, supply chain and e-commerce. He got pissed by the traditional tech stack and turned into analytics engineer thanks to dbt. He likes good filter coffee, open source projects, and filmography.
Matej ChomaSenior Researcher, Meteopress
Matej is a Data Scientist at Meteopress and a Ph.D. student at FIT CTU. Specializing in spatiotemporal prediction and physics-informed deep learning. Nature lover and mountaineer.
Pavel JezekData Engineer, STRV
Pavel is a data and analytics engineer at STRV who turns great coffee into business value. He enjoys problem solving and finding efficient solutions to help push the projects forward. He is a fan of unix and FOSS and enjoys spending his time in the command line.
Stepan KadlecML and Data Engineering specialist, ForML
ML and Data Engineering specialist focusing on operational architectures of ML solutions - enabling their smooth transition from research to production through appropriate ML lifecycle management. Previously leading research in ML pipeline formalization at Oracle AI Apps. Co-author of the ForML framework.
Mike PearmainChief Data Officer, VietcomBank
Currently Chief Data Officer at VietcomBank with a special interest in products with ML components, the architectures to support these, and organizational separation of concerns to deliver them. Previously a data scientist at Google, Kaggle competitions master, and co-author of the ForML framework.
Practical & Inspiring Program
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (workshops won't be streamed)
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Operationalizing Responsible AI in Practice
Mehrnoosh Sameki, Microsoft
Are you a data scientist looking to author machine learning solutions responsibly using the latest tooling? Our brand-new Responsible AI dashboard is designed to help you by providing a single pane of glass bringing together a variety of model assessment and responsible decision-making capabilities under one roof. The dashboard enables you to easily assess and validate your models by looking into a variety of model performance fairness and error analysis components interpret your models (including blackbox ones) to understand how they are making their predictions perform perturbations via what-if analysis and counterfactual analysis and understand/fix data imbalance issues. By the end of this session you will have gained hands-on experience in the utilization of these tools and how you can use the outputs to identify diagnose and mitigate your models’ issues and communicate their value to your stakeholders across the organization.
Gaussian process regression when it comes to numerical simulators
Thomas Browne, Kiwi.com
While numerical simulators are often used by heavy industries to model complicated phenomenons their complexity makes them sometimes slow and harder to exploit. Gaussian process regression (GPR) provides an accurate framework where based on a limited amount of calls to the simulator one can have a prediction on any of the simulator's output together with confidence bounds. GPR can then be extended to solve optimization and sensitivity analysis tasks with a parsimonious approach. In this workshop the attendance will be given a walk through the basics of GPR in Python. Besides they will be provided with implemented examples how GPR can help with concrete assignments.
Drug discovery using NLP
Aisling O’Sullivan, Dataclair
You might have seen many exotic use cases of (large) language models - they can summarize long volumes of text into a few sentences they can write a brand new body of text or generate an original text adventure game. We will do neither of those things. Instead we will explore if we can make language models understand more abstract contexts hidden within the text e.g. molecule structures and other chemical properties. Specifically we will reproduce a particular article which is using NLP methods to support the drug discovery process.
Learning to Learn: Hands-on Tutorial on Using and Improving Few-Shot Language Models
Michal Štefánik, Gauss Algorithmic
As AI models become an increasingly common element of many applications we more notoriously face practical limitations of specialized models working well only for a single training task and data. Huge language models like OpenAI's GPT-3 showed that models could be much more versatile and adapt to new tasks without updating the model provided only with natural instructions and a small number of input-output examples of the desired task. In practice Few-shot learners can solve your new task with accuracy comparable to the supervised models trained on hundreds to thousands of samples. Our workshop will give you an overview of the existing models able of Few-shot learning including their limitations. We will experiment with creative ways of utilizing in-context Few-shot learning such as customizing the model's predictions to specific users. Finally we will provide some recipes for training Few-shot learners for new languages or further scaling up the accuracy of existing Few-shot models.
Reproducible, portable, and distributable ML solutions in Python
Stepan Kadlec, ForML
When achieved the combination of reproducibility portability and distributability in ML solutions constitutes a powerful faculty unlocking a number of operational opportunities. While reproducibility is a well-established pathway for conducting scientific research it is not always receiving the same recognition within the data product industry. Similarly portability and distributability are typically regarded as irrelevant for bespoke solutions and only pursued in case of explicit demands. This might be reasonable given the extra cost incurred by conventional development; but with modern tooling these properties can be easily achieved without much extra effort. In return this brings significant benefits in the form of highly collaborative R&D inherent lifecycle management effective model troubleshooting carefree and flexible deployment (latency/throughput-optimal runtime modes) and even potential commoditization (market of turnkey solutions). In this workshop we will dive deeper into these concepts examining carefully the available technologies and reviewing some of the existing tools. A significant amount of the time will be spent working with the ForML framework implementing a practical end-to-end ML solution demonstrating all of these declared principles.
Machine Learning in Observability
Kurt Kuppens, Solarwinds
Leveraging Machine Learning to power Observability of increasingly complex IT systems. This workshop covers how observability platforms leverage various data sources like Logs traces performance metrics and configuration changes to build ML features/apps that power observability. The hands-on session covers how ML techniques like Anomaly Detection Forecasting Language Modelling and Graph modelling are leveraged to process the data in real time and provide insights to improve performance indicators like MTTR MTTI etc.
Bayesian Networks in business planning and risk management
Martin Plajner, Logio
Explore with us a complex and powerful family of models Bayesian Networks. In our workshop you will have a chance to i) understand the Bayesian Network models and their strengths drawbacks and application areas ii) build a data-based model which you will use to answer business planning questions and what-if scenarios and iii) create an expert-knowledge model to handle risk management infer posterior probabilities and construct emergency scenarios. In this workshop you will have an opportunity to get hands-on experience with Bayesian Networks modeling using R language. No prior Bayesian Networks knowledge is required bring a laptop with the current R version ready to use.
Predicting weather with deep learning
Petr Šimánek, FIT CTU
In this workshop we will implement train and test machine learning models that analyze satellite and weather radar data. You will get hands-on experience with the most common deep neural nets used for spatiotemporal predictions (e.g. UNet with some bells and whistles and convolutional recurrent nets). You will play with PyTorch implementation and analyze the results. You will understand the common pitfalls and reasons why the prediction fails.
ML with a Large Set of Variables: Feature Selection Techniques for Regression in Python
Aneta Havlínová, Workday
In many ML applications we encounter a situation when datasets have a large amount of potential features but relatively few observations—from an analysis of genetics data with thousands of gene expressions through financial data modelling with voluminous data that flows in from capital markets and economies to HR analytics area with extensive data on employees such as their personal information skills job histories and more. In these cases feature selection is crucial to prevent overfitting and to improve model performance. This workshop provides participants with an overview of some of the widely used feature selection methods including linear models with Lasso and Ridge regularization or tree-based methods such as Random Forest and its extensions. First a theoretical background is presented. Afterwards the participants are guided step-by-step through implementation of these methods in Python with the practical use-case being tied to the HR data analytics context.
Dbt Jaffle Shop with ML
Jozef Reginac, STRV
Dbt has gained significant traction in the analytics engineering community and is on the quest to become the go-to tool for data teams. With the latest addition Python models it’s becoming relevant even for machine learning engineers. We would like to walk you through the basic project setup the first data model all the way up to creating the Python model. Our goal is for you to be confident in using dbt in your team and to help you merge the work of all data team members into one environment.
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (and on-line)
Registration from 8:30
Conference day 1
O2 Universum, Českomoravská 2345/17a, 190 00, Praha (and on-line)
Doors open at 08:30
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 2023 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
Communities and Further support
Would you like to present your brand to 1000+ Machine Learning enthusiasts? Send us an email at firstname.lastname@example.org to find out how to become a ML Prague 2023 partner.
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If you have any questions about Machine Learning Prague, please e-mail us at
Scientific program & Co-Founder