Barbara Sobkowiak
Data Scientist - Project Head
Data Scientist - Project Head
Poznań, Poland
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I have been involved in data analysis of all kinds for more than 10 years, as I started my adventure with spatial data i.e. GIS. Data tabular, linguistic or spatial are not scary to me. Currently, I focus on machine learning and AI solutions. In my career I was also for many years a manager of a data science team, delivering ML end-to-end projects on both on-premise and cloud platforms. I combine two worlds on projects: technology and business, while ensuring that the project is executed following good practices and that the model supports users' activities. I strongly believe that to solve clients' business problems, you need to talk to them and understand their issues, and only then create machine learning models. I also support the Poznan chapter of Women in Machine Learning and Data Science (WiMLDS) in activities supporting women in this area. In my free time, I explore squash and new corners of the world, especially from aboard a sailboat. And on long winter evenings, I like to watch a good detective story or a documentary about the world of sports.
Od ponad 10 lat jestem związana z analizą danych każdego rodzaju, bo swoją przygodę zaczynałam z danymi przestrzennymi tzn. GIS. Dane tabelaryczne, językowe czy przestrzenne nie są mi straszne. Obecnie skupiam się na rozwiązaniach machine learning i AI. W swojej karierze byłam również przez wiele lat managerem zespołu data science, dostarczającego projekty ML end-to-end, zarówno na platformach on-premise jak i chmurowych. Łączę na projektach dwa światy: technologiczny i biznesowy, dbając przy tym, by projekt był wykonany zgodnie z dobrymi praktykami, a model wspierał działania użytkowników. Mocno wierzę w to, że aby rozwiązywać problemy biznesowe Klientów, trzeba z nimi rozmawiać i rozumieć ich zagadnienia, a dopiero potem tworzyć modele machine learning. Wspieram również poznański oddział Women in Machine Learning and Data Science (WiMLDS) w działaniach wspierających kobiety w tym obszarze. W wolnym czasie odkrywam squasha i nowe zakątki świata, zwłaszcza z pokładu żaglówki. A w długie zimowe wieczory lubię obejrzeć dobry kryminał lub dokument o świecie sportu.
Links
Area of Expertise
Topics
WomenTech Mentoring Program - GFT Poland
Topic: A chatbot that knows your business. Is this possible and how?
Internal mentoring program for women. On the graduation day, all mentees prepared a presentation - only 10 minutes. I talked about building chatbots, how to do it, and what kind of challenges could occur. But what was unusual, I didn't use slides, instead bricks. And everything had to be understandable for people, who don't know AI. And it was, was confirmed a mangers from business units in the company, who were there.
Toastmasters Poznań - AI workshop
Topic: Myth busters - artificial intelligence (AI)
Abstract: The last year has been a real hype for Artificial Intelligence. A lot of myths and beliefs have accumulated over the past years. AI is, despite appearances, not a magic wand, nor a product of the last year. I will try to correct some of these myths while conveying very lightly the basics of this world. And all this will be accompanied by some gamification
AI workshop - myths for a very diverse audience - people with a different understanding of IT and experience with different faces of AI. The workshop was conducted during a regular weekly meeting of the Toastmasters Poznań Club.
Data Science Summit
Topic: The model is your smallest problem, or the challenges of implementing AI/ML projects in an organization
Abstract:
On the one hand, there is a growing number of companies boasting of having AI/ML solutions, on the other hand, according to Gartner data, more than half of the projects do not make it from the pilot phase to the production phase. In addition, the implementation itself is often prolonged in time, and the process resembles a veritable runmageddon. Is it always the algorithm that is the problem? Or do many challenges involve issues that are not at all related to mathematics? The model is a very important piece in the whole puzzle necessary for the smooth implementation of an AI/ML solution in an organization on a large scale, but not the only one. If so, what are the others and how can you deal with them, you will hear about them during this presentation.
Masterclass - Britenet Technology Conference
Topic: Maneuvers with artificial intelligence, or how to survive and not get caught in the entanglements of an AI project
Abstract:
You've probably heard a lot about how great opportunities AI offers.
Maybe you've even considered taking part in such a project yourself someday as a programmer, DevOps, project manager, business analyst, or even in a completely different role. If so, great, just you have to be careful, because an operation entitled. "We are implementing AI," is not a standard IT project. And regardless of whether you have been involved in many big missions, I mean projects or are just a fledgling cadet. Such a project can only be carried out by a special task team with the skills to negotiate, dive into the mud, clarify the unexplained, and show extraordinary perseverance.
Why?
Because the data is a mess bigger than the Augias stable, the client expects 99% accuracy, and the flexibility in planning would not be averse to even the best Agile Coach. What such a project looks like, what you have to deal with, and why all this is not so obvious and ongoing, plus how to deal with it so as not to fall into any entanglements and traps, and to complete the mission successfully, about this and you will learn at this presentation. After that, you will be able to use the same language that data scientists speak, which will make it easier for you to accomplish your tasks on AI projects. Rest assured, you won't need knowledge of statistics or programming, just an open mind. Remember, though, that the only opportunity to gain access to classified data that few people talk about in machine learning courses ;)
GHOST Day: Applied Machine Learning Conference
Topic: Time Series - it is not as easy as you think
Abstract:
For many people, time series is often a rather trite topic, and forecasting sales or orders is trivial. Either the issue ends with ARIMA or, conversely, there is a belief that it is enough to throw the data into neural networks. After all, it can't be that hard, since logistics and retail companies somehow manage to do it, and on Kaggle the data is so nice.
Nothing could be further from the truth. With an assortment of a few dozen or thousands of products, several to several thousand locations, and daily granularity, the issue is no longer so simple.
When you're modeling a demand forecast, several questions arise - how to deal with infrequent sales, what evaluation metric to choose, how much impact business decisions have on the process, and others. What can happen on this type of project, what will surprise you with the data and the business and what are the options for solving them I will tell you in the presentation.
DBA - Digital Banking Academy
Topic: Go big with AI, or 3 possible paths for its implementation.
DBA - Digital Banking Academy
Topic: 5 lessons from AI implementations in Retail
Warszawskie Dni Informatyki (Warsaw Computer Science Days)
Topic: Optimization with success? All it takes is 5 steps
Video - https://www.youtube.com/watch?v=OsWkWH6zPXU
Kongres Nowoczesnej Dystrybucji (Congress of Modern Distribution)
Topic: The future is today. Bring AI into promotional planning.
FTB - Forum technologii Bankowych (Banking Technology Forum )
Topic: In pursuit of Machine Learning, or some principles of how to approach an AI project
Video: https://www.youtube.com/watch?v=X3kRU_rDOGM
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