Conference presentation and roundtables will be grouped according to the following schema.
Official conference language is English.

9th June



The agenda for this session features live presentations prepared by our speakers from the United States. Due to the time zone differences, this will be a special afternoon session, throughout which a significant amount of time is intended for live Q&A sessions.


Welcome and introduction
CEO & Meeting Designer


10 common mistakes creating AI products for Enterprises

Successful AI products delight customers and drive meaningful business results. However, many data science initiatives result in lengthy POC's that are hardly used. In this workshop, Jaekob will share his journey building AI products at large enterprises such as Adobe, Oracle, and startups. The lessons learned help identify the areas of investment where AI yields the best outcomes.



General Partner, Product Alliance


Time for Q&A


Trust in Numbers: An Ethical (and Practical) Standard for Algorithms

"Who was the real Tara Simmons? A court was forced to decide whether she was a convicted felon (her past) or a respected lawyer (her future). Algorithms that use the past to predict the future are common but can cause great harm. Via the story of Ms. Simmons and others like her, we develop a practical ethical standard (with currently available tools) for evaluating and comparing algorithms. Building on recent work by IBM and the IEEE to define ethics for the use of artificial intelligence, this session seeks to set forth a practical and measurable standard for algorithms that may be used to improve ethics of an individual system and to compare the relative strengths and weaknesses between them.



Senior Identity Strategist
SailPoint Technologies


Time for Q&A


Bigdata, ML & AI Fundamentals for Managers – A pragmatic view beyond hype!

Big Data is not simply about data management problems that can be solved with technology. Instead, it is about business problems whose solutions are enabled by technology that can support the analysis of large sets of potentially diversified data. For this reason, this talk will include two key parts. Part one will be on business-focused discussion that sets the stage for the technology-focused topics covered in Part II. Topics include (1) Business Drivers for Adopting Bigdata, (2) Business Adoption and Planning Considerations, (3) Business Intelligence with Big Data techniques, (4) Technology building blocks & implementation workflow plus (5) Establishing & Evaluating Business Success.



Adjunct Professor, Senior Certified Technical Trainer
New York Institute of Technology, Arcitura Education 
Analytics Research Scientist
Southern Alberta Institute of Technology (SAIT)


Time for Q&A


Production-grade ML Pipelines - From Data To Metadata

It is well known that data quality and quantity are crucial for building Machine Learning models, especially when dealing with Deep Learning and Neural Networks.
But besides the data required to build the model itself, there is another often overlooked type of data required to build a production grade Machine Learning Platform: Metadata.
Modern Machine Learning platforms contain a number of different components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature stores, and many more. Most of these components have associated metadata including versioned datasets, versioned Jupyter Notebooks, training parameters, test/training accuracy of a trained model, versioned features, and statistics from model serving. For the dataops team managing such production platforms, it is critical to have a common view across all this metadata, as we have to ask questions such as: Which Jupyter Notebook has been used to build Model XYZ currently running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated? In this talk, we look at existing implementations, in particular MLMD as part of the TensorFlow ecosystem.

Head of Engineering and Machine Learning


Time for Q&A


Case studies on application of Machine Learning in metals manufacturing

In metals manufacturing, the operations environment is full of data collected by thousands of sensors at a very small interval. Historically, decision making was based on traditional descriptive analytics techniques. However, with the advent of “Industry 4.0”, Machine Learning is quickly becoming an important decision making tool. Novelis, through models deployed within melting and rolling operations, has already seen a great value from these techniques. This talk will focus on a couple of such model that added tremendous value to Novelis’s remelt and cold rolling operations.



Director of Data Science


Time for Q&A


Towards Human-AI teaming: Distributed Multi-Agent system and Human Collaboration

The goal of our presentation is to stress the need for Human-in-the-Loop and Hybrid-Systems based thinking in AI driven systems. We present solutions with experimental results to formulate and implement such systems. During the session you will hear about the distributing learning processes to build automated AI systems, find out how to ensemble created models to accelerate next-level training processes as well as listen about the architecting communication to facilitate human-AI interactions. We will also focus on the designing operational structure of agents and humans to arrange their communications and evaluating component-level performance of Human-AI teaming to optimize human and AI efficiencies.

Product & R&D strategy
AI Redefined Inc
AI Redefined Inc


Closing and summary
CEO & Meeting Designer


Better Understand Your Documents with AI

Cloud Advisor

AI-based systems for monitoring of earthquakes

Research Scientist
Stanford University

Foundations of Data Teams

Managing Director
Big Data Instutite

Banking with Backbot. An AI Powered Chatbox using AWS Lex

Director of Prototyping

Artificial Intelligence is now!

MIPU Predictive Hub
10th June


There will be multiple sessions delivered via online conference platform

Opening remarks
CEO & Meeting Designer

Plenary session

How to streamline MLOps with Vertex AI

Most probably you already build and use AI in your company or plan to do so. You may wonder how to make your machine learning delivery to be more efficient, reliable and so it continuously brings value to your business. In this session you will learn more about how to streamline your machine learning operations (MLOps) with Vertex AI, a newly launched, managed end-to-end ML platform from Google.


Customer Engineer
Google Cloud

Short break

Parallel tracks I

The parallel sessions are divided into three categories. Participants can choose from:

♦ Explainable AI and ♦ Data and Machine Learning for Managers
♦ Hands-on, ♦ Computer Vision, ♦ NLP and ♦ Deep Learning
DATA Session:
♦ Data Engineering and ♦ MLOps

Case for the AI regulator

Associate Director Data Science
Publicis Sapient
Director Data Science & Analytics
Publicis Sapient


Text Summarization with Transformer Models – Exploring New Frontiers in NLP

Data Scientist
qdive GmbH

Scalable AI deployment on the edge

Professor of data science, co-founder
Tilburg University, Scailable

Inspirational applications of computer vision in healthcare and agriculture.

Analytical Consultant


The network for the next decade: AI Driven. Cloud Enabled. Agile

Sales Lead for AI Driven Enterprise


Data Science Lifecycle & ops – MLOps in Azure

Digital Advisor



Building, deploying and operating Machine Learning Models with Tensorflow on Google Cloud Platform

Cloud Engineer, Data&AI
Chmura Krajowa


On Pushing the Frontiers: Deep Learning in Space

Head of AI, KP Labs Assistant Professor
Silesian University of Technology

Finding duplicate images made easy in python with imagededup

Senior Machine Learning Engineer
Axel Springer AG

Short break
♦ Explainable AI and ♦ Data and Machine Learning for Managers
♦ Hands-on, ♦ Computer Vision, ♦ NLP and ♦ Deep Learning
DATA Session:
♦ Data Engineering and ♦ MLOps

Content readiness of clients and the implications for your project

Technical Project Manager
Springbok AI

Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models.


Co-founder, Chief Data Scientist, Assistant Professor
Data Juice Lab, University of Warsaw
Co-founder, CEO
Data Juice Lab

Two-Layer Approach to Combine Artificial and Human Intelligence when Labeled Data is Scarce

Senior Data Scientist

Topological Driven Methods for Complex Systems

Data Scientist
Cisco Systems France

From ML Research to Production - the Autobahn Way!

Managing Director
Dat Tran Ventures

Implementing a ML data product for lead management with focus of the insurance industry

Strategy & Data Science
Syncier Analytics

Implementing AI successfully and using it to add value to companies

Founder and CEO GmbH

Can AI fix the global food waste problem?

Founder & CEO GmbH

Scaling data function in SMB, DATA ENGINEERING

Director of Engineering, Head of Data Platforms and Infrastructure

Short break

Roundtable discussion session

Parallel roundtables discussions are the part of the conference that engage all participants. It has few purposes. First of all, participants have the opportunity to exchange their opinions and experiences about specific issue that is important to that group. Secondly, participants can meet and talk with the leader/host of the roundtable discussion – they are selected professionals with a vast knowledge and experience.

1. AI is for everyone

With the advancement of tooling in the space of ML, AI is not anymore the domain of research labs. Let's talk about how AI is utilized and used in your company today and what are the best practices to leverage the value of AI for all business areas.

Head of Google Cloud, Central & Eastern Europe
Google Cloud
Customer Engineer
Google Cloud


2. Implementing AI in Enterprise - questions to resolve

There are many interesting AI solutions - but how to choose the right one for your organization? Where may AI deliver the most substantial gain- how to locate these places in your business? How do implement it - it is advisable to build your in house AI knowledge or better to find a reliable partner?

Head of Data Science
Ringier Axel Springer Polska


3. Methods of detecting undesirable events based on machine learning.

Łukasiewicz Research Network - Institute of Innovative Technologies EMAG as a research institute and collaborator in research and development projects is involved in numerous and divers challenges related to artificial intelligence and machine learning. During the round table, I would like to participate in a discussion on identifying undesirable events with the use of machine learning and knowledge discovery approaches. I can share our experience in two recent case studies. The first one is for cybersecurity. In this example, ML methods are used to identify suspicious behaviour in the monitored network traffic. The second use case is for predictive maintenance. In this example, ML methods are used to identify machine failure.

Senior specialist - R&D manager, data analyst
Łukasiewicz Research Network - Institute of Innovative Technologies EMAG


4. Can federated learning save the world?

The usage of AI models is increasing worldwide. However, traditional AI setups with central training in a cloud or a server have several disadvantages: leak of data privacy, high latency, complex infrastructure, and high energy consumption in data centers due to cooling. Federated learning brings AI training to the data and resolves these mentioned problems by design. We will answer questions as to what federated learning is, can it be used for your project, and how can it be implemented in your existing projects?

Program Manager
Adap GmbH


5. What is the craze about MLOPS

Why is MLOPS now poping up? What is the role of MLOPS? Which skill set is needed in order to be a good candidate for ML Ops? Is ML Ops part of the development team or a separated team?

Senior Data Scientist

Lunchtime break

Tech battle

During the battle, we fight and show the best sides of a given technology and the drawbacks of the opponent's solution. This time we take Keras that will be defended by Karol and PyTorch that will be represented by Piotr. We cover the maturity, community, usage examples, prototyping, production deployment, distributed training, and available extensions. The audience is allowed to join the battle!


Software Developer, Data Scientist


Senior Software Engineer

Parallel tracks II

The parallel sessions are divided into three categories. Participants can choose from:

♦ Explainable AI and ♦ Data and Machine Learning for Managers
♦ Hands-on, ♦ Computer Vision, ♦ NLP and ♦ Deep Learning
DATA Session:
♦ Data Engineering and ♦ MLOps

Harnessing the virtual realm for successful real world artificial intelligence.

Artificial Intelligence Developer Relations (AI DevRel)

User Segmentation: Conversion Likelihood Model

Senior Data Scientist
Ebay Classified Group (eCG)

The Data Mesh

Head of Data
UNIQA Insurance Group AG

Why Deep Learning cannot match the human brain?

Head of Artificial Intelligence
evocenta GmbH

AI methods for predictive maintenance in production processes

University of South Westphalia

Building End-to-End Machine Learning Workflows with Kubernetes, Kubeflow Pipelines, and BERT

Sr. Developer Advocate, AI & Machine Learning
Amazon Web Services (AWS)

Mitigating Privacy Risks in Machine Learning through Differential Privacy

Fraunhofer AISEC

Stock Price Prediction and Portfolio Optimization Using Recurrent Neural Networks and Autoencoders

Senior Consultant

Measuring success of Data Teams

Head of Data Analytics

Short break
DATA Session: ♦ Data Engineering and ♦ MLOps

From gut feeling to algorithm: Leveraging AI to transform the product distribution in the insurance industry

AI-based chatbots: opportunities, possibilities, limitations.

General Manager DACH

Personal bandit or: how to give users what they want (on a budget)

Lead Data Scientist
eBay Classifieds Group

Knowledge Graph Based Entity Similarity Learning

Data Scientist
Delivery Hero SE
Senior Data scientist

Application of time series forecasting and optimization tools for management of cash supply chain.

Artificial Intelligence and Process Design specialist
BNP Paribas
Artificial Intelligence & Project Management, Team Manager
BNP Paribas

Aerial Remote Maintenance

FlyNex GmbH

From zero to hero – how to build open source platform for enterprise users

AI Team Leader
Bank Millennium

Solutions Architect
Bank Millennium

Special meeting

Panel discussion - Does AI need state support?

An increasing amount of funds and advancing efforts are put into AI worldwide, while a big part of this operates from public sources. Why is it like this and where and how make it meaningful - at development, proliferation and deployment? To what extent governments should get involved here - to make things go faster but not to spoil the natural, free-market forces? Is there now such a "competition" between particular countries in the World? Is it good for all of us? What seems to be most effective and needed now?

The panel will be joined by:


Managing Director
Digital Poland
Head of the Jagiellonian Human-Centered AI Laboratory (JAHCAI)
Jagiellonian University,
Professor & Director of UK Financial Computing Centre
University College London
Director, Technological Infrastructure Division
Israel Innovation Authority
Co-Founder & CEO
ValueWorks GmbH
11th June


In each round there will be a slot for complete 4 hours workshop of your choice. Detailed description is available here.

Round I




Software Developer, Data Scientist

Extra workshop


Cloud Engineer, Data&AI
Chmura Krajowa
Specialist, Data&AI
Chmura Krajowa

Round II


Neural networks are currently the most popular machine learning methods. One type of use cases are based on pattern recognition on images. Neural networks as any other solution is liable to security issues. In this training we go through potential leaks and vulnerabilities of neural networks. This training is dedicated to managers and data scientists that want to learn more on how to find leaks and secure a neural network.




There are many great tutorials for training your deep learning models using TensorFlow, Keras, Spark or one of the many other frameworks. But training is only a small part in the overall deep learning pipeline. This workshop gives an overview into building a complete automated deep learning pipeline starting with exploratory analysis, over training, model storage, model serving, and monitoring.


Head of Engineering and Machine Learning

AI@Enterprise Summit 2022

Practical applications of Artificial
Intelligence in business



Evention Sp. z o.o
Rondo ONZ 1 Str, Warsaw, Poland


Anna Kocik
m: +48 533 374 006

© 2023 | This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.