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

9th June



In this online session there will be live presentations by our speakers from the United States. Due to the time zone differences this is a special afternoon session. Also some time is for live Q&A sessions.


Welcome and introduction
CEO & Meeting Designer


10 biggest mistakes when creating AI products

Successful AI products delight customers and are habit forming. However, many data science initiatives result in lengthy POC's that sit on the shelf or chatbots that are hardly used. In this workshop, you will learn 10 strategies that ensure that your data science projects have a material impact on the customer experience and the business.



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 


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

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

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

Sales Lead for AI Driven Enterprise


To be announced

Data Science Lifecycle & ops – MLOps in Azure

Digital Advisor


To be announced

To be announced

To be announced

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

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

Topological Driven Methods for Complex Systems

Data Scientist
Cisco Systems France

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

Implementing AI successfully and using it to add value to companies

Founder and CEO GmbH

AI in the greenhouse - for happy plants and a healthy planet

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

Strategy & Data Science
Syncier Analytics

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. 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?

AI & Data Director
Ringier Axel Springer Polska

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!

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)

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

Why Deep Learning cannot match the human brain?

Head of Artificial Intelligence
evocenta GmbH

User Segmentation: Conversion Likelihood Model

Senior Data Scientist
Ebay Classified Group (eCG)

Establishing an Enterprise-wide Data Strategy

Head of Data
UNIQA Insurance Group AG

Mitigating Privacy Risks in Machine Learning through Differential Privacy

Fraunhofer AISEC

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)

Short break
DATA Session: ♦ Data Engineering and ♦ MLOps

Numerical computation in Python - Slow or Fast?

Data Science/AI Lead
Flyr Inc

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

Senior Consultant

Predicting customers errors

Head of Data Analytics

Knowledge Graph Based Entity Similarity Learning

Data Scientist
Delivery Hero SE
Senior Data scientist

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

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

AI Team Leader
Bank Millennium

Solutions Architect
Bank Millennium

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

To be announced

From ML Research to Production - the Autobahn Way!

Managing Director
Dat Tran Ventures

Special meeting

Panel discussion: should AI development, proliferation and deployment be supported by the governments? If yes, to what extent? What seems to be most effective and needed now?

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

Round II

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

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