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

, 2023


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

Phenomenal Speakers

Practical & Inspiring Program


O2 Universum, Českomoravská 2345/17a, 190 00, Praha (workshops won't be streamed)


Room D2 Room D3 Room D4 Room D6 Room D7
coffee break

Operationalizing Responsible AI in Practice

Room D2

Mehrnoosh Sameki, Microsoft
Michal Marusan, 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

Room D3

Thomas Browne,
Lucie Blechová,

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 attendants will be given a walk through the basics of GPR in Python. Besides they will be provided with implemented examples how GPR can help.

Drug discovery using NLP

Room D4

Aisling O’Sullivan, Dataclair

NLP is an important and rapidly growing field. While its application in fields such as language translation and chatbots is well-known the use of NLP in the billion-dollar pharmaceutical industry is less commonly cited. NLP is particularly appealing to drug discovery since these models are capable of capturing complex medical concepts that are difficult for humans to grasp as well as understanding the structure of molecules which are key to discovering novel drugs. In this workshop you will be introduced to the world of using machine learning for drug discovery with a focus on NLP. We'll show you how to apply ML techniques to discover novel drug candidates using NLP on the text and also by applying NLP to the "language of molecules."We will do this through a use-case of classifying molecules that can or cannot cross the blood-brain barrier. This use-case is important for developing drugs that target diseases of the central nervous system (such as Alzheimer's) as well as for identifying potentially toxic drugs. We'll also explain the applicability of these approaches to other important problems such as identifying antibiotics and cancer drugs.

Learning to Learn: Hands-on Tutorial on Using and Improving Few-Shot Language Models

Room D6

Michal Štefánik, Gauss Algorithmic
Nikola Groverová, 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

Room D7

Stepan Kadlec, ForML
Mike Pearmain, VietcomBank

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.

coffee break

Machine Learning in Observability

Room D2

Kurt Kuppens, Solarwinds
Venkata Pappu, 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

Room D3

Martin Plajner, Logio
Theodor Petřík, 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

Room D4

Petr Šimánek, FIT CTU
Jiří Pihrt, FIT CTU
Matej Choma, Meteopress

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

Room D6

Aneta Havlínová, Workday
Martin Koryťák, 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

Room D7

Jozef Reginac, STRV
Pavel Jezek, 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.

Conference building

O2 Universum
Českomoravská 2345/17a, 190 00, Praha 9


O2 Universum
Českomoravská 2345/17a, 190 00, Praha 9

Now or never Registration

Early Bird

Sold Out

  • Conference days € 240
  • Only workshops € 170
  • Conference + workshops € 390



Last 100 registrations

  • Conference days € 290
  • Only workshops € 240
  • Conference + workshops € 490

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

Thank you to Our Partners

Co-organizing Partner

Platinum Partners

Gold Partners

Communities and Further support

Would you like to present your brand to 1000+ Machine Learning enthusiasts? Send us an email at to find out how to become a ML Prague 2023 partner.

Become a partner

Happy to help Contact

If you have any questions about Machine Learning Prague, please e-mail us at


Jiří Materna
Scientific program & Co-Founder

Teresa Caklova
Event production

Gonzalo V. Fernández

Jona Azizaj