The most complicated algorithms, revolutionary inventions, and technologies all consist of hundreds of the simplest components that use create synergy together. The ability to understand and create such projects depends on decomposition and simplification. On March 14 at the Data Science UA Conference, we will explore in details the experience of engineers, product managers, and investors in data science products.
Mohamed-Achref has a background in computer science and engineering. He has been working on deep learning applications for car diagnostic and visual quality inspection.
The talk would deep dive into technical aspects such as loss optimization strategies, mathematical properties, optimizing and satisficing metrics, hyper-parameter tuning, components for ML pipeline automation, application for car diagnostic and image classification with neural networks.
Luis has a Ph.D. on Machine Learning and a history track of publishing/serving (50+ pubs.) on the major Data Science venues worldwide such as KDD, AAAI, IEEE TKDE or ECML/PKDD. His path in Industry encompasses a long successful track on deploying ML methods in commercial products with proven added value throughout the Finance, Mobility, Retail and Energy industries, from EMEA to APAC. Recognized as atypical for an aptitude in presenting complex AI concepts in a translated manner for general audiences, being regularly invited for Keynotes worldwide (ranging from Brisbane, Australia to Las Palmas, Spain).
In this talk, Luis will be uncovering the common pitfalls on bringing applied DS projects to life in Finance and some of the state-of-the-art solutions that his team brought up to address them. Curious for more? See you there.
For the last seven years, he has been actively researching and developing computer vision and natural language processing systems. He is the author of a machine learning course on the Prometheus platform and an in-depth training course at the ARVI Lab.
He has extensive experience in video processing using deep learning methods for detecting objects and actions, predicting image depth maps, semantic segmentation and generating subtitles for images and video studios in Hollywood.
He has developed one of the first automation systems to control the placement of groceries at the store shelves using neural networks. He led the development of many projects for automated analysis of news in various languages, recognition of entities, analysis of conceptual drift and representation of language structures using machine learning systems.
Borys graduated from the Chair of Physical and Biomedical Electronics of the KPI with honors in 2007 on the specialty “Physical and Biomedical Electronics”. He defended his dissertation at the Faculty of Electronics in the KPI in 2012.
Borys works CTO at Scalarr.
He will tell why ML model training is not the main task for model deployment to production.
Also go through the Kubeflow setup and pipelines maintain.
And he will show a real case of how it changes the philosophy of ML team and how they write more standard code for production deployment.
Many industrial workspaces remain extremely dangerous for workers. Steel plants, construction sites, oil rigs, mines, and other industrial environments have high rate of accidents, including fatal.
Our company is on a mission to radically improve worker safety with computer vision, deep learning, and sensor fusion. Unlike typical applications of computer vision, we are confronted with harsh environments, low visibility, lack of basic data communications, and difficulty in obtaining training datasets. All of that calls for highly innovative solutions and thinking outside the bounding box.
Vladislav studied at the KPI at the Faculty of Informatics and Computer Engineering, where he defended a diploma about Neural Architecture Search based on Reinforcement Learning.
He’s currently working on creating efficient neural representations of scalar fields. On previous projects, he worked on Computer Vision problems (mostly generative models), NLP and time series analysis.
Today meshes are used ubiquitously to represent 3D shapes. They’ve been around for a long time and several optimizations have been developed to rasterize them efficiently. However, they still have a few drawbacks which limit their possible applications.
NeoRender is a company, which strives to create a new way to represent 3D shapes, which will be free of such drawbacks. On this talk, you’ll get a glimpse of what NeoRender doing on the example of a rigged (animable) 3D model of humans call SMPL. You will learn how to convert textured 3D meshes to neural network representations, which then can be used to approximate physical interactions efficiently and also rendered in a variety of poses.
ML Engineer with a wide range of expertise in data science engineering who participated in Recommendation Systems, Computer Vision, Big Data projects. Holds two master degrees in Theory of Probability and Mathematical Statistics, and Economics. Has a highly positive attitude towards applying ML and Data Science in the most suitable way. He has a lot of interests in Sciences, Philosophy, Sports, and combines it in everyday work.
Tracking objects in Sports requires specific complex solutions: how to combine multiple NNs in one modular system to solve different tasks like object detection, classification, clusterization, etc. How to incorporate domain knowledge to increase the accuracy of tracking. Which classical video processing methods allow improving tracking. How to test separated modules and systems in general. Examples of video from football games will be presented.
Maryna is the leader in Digital HR and focuses on organizational performance improvement and HR management. Maryna joined EY in 2015. She specializes in analysis of processes efficiency, HR automation, strategy development, IT systems benchmarking and selection, organizational structures and processes redesign.
Maryna consults companies on employer branding, conducts studies in the field of integrated talent management. She is a speaker at public events, trainer at trainings and seminars. Maryna holds a National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute diploma the with a major in Economic Cybernetics.
Yuliia joined EY in 2014. She specializes in labor market studies and HR management.
Yuliia conducts compensation and benefits surveys in Ukraine and CIS, develops grading pay structures. She has experience in integrated HR management , HR audits, short and long-term employee incentive programs development.
Yuliia graduated from Taras Shevchenko National University of Kyiv with a degree in Enterprise Economics.
Michael is a data strategist, NLP expert and systems architect .
Successfully implemented dozens of large projects, including projects for the US government and achieved state-of-the-art results in QA systems.
He runs his own data science R&D company and coordinates several companies in the development and delivery of AI software and solutions for IT, Healthcare, Finances, Cybersecurity, Retail and other fields.
His talk will cover the most common pitfalls during general and domain-specific question-answering systems development. The talk also describes 17 helpful patterns for how to avoid these pitfalls. Besides talk, you will learn how to manage question-answering systems design, functionality, automation, scalability and cost reduction. The presentation will also help you comprehend the most common operational aspects of question-answering systems, based on existing experience. For a wide audience this knowledge can be extrapolated to NLP and software engineering in general.
Mykola Maksymenko, R&D Director at SoftServe, drives technological development in applied science and AI, human-computing interactions, and sensing. Mykola holds a Ph.D. in Theoretical Condensed Matter Physics, with over ten years of research experience, previously working at the Max Planck Institute for the Physics of Complex Systems and the Weizmann Institute of Science.
Quantum Computing promises a lot of potential to many computationally intense areas, but so far most of the novel algorithms are tested on classical simulators of quantum processors. Here Tensor Networks are one of the backend workhorses for efficient compression of quantum state and manipulations with quantum logic gates.
On the other hand, Tensor Networks can be seen as a new trainable Machine Learning object, in some cases being more expressive than Deep Neural Networks. It also allows to compress existing DNN architectures and speed up inference times.
I will outline what Tensor Networks are from physics perspective to Machine Learning and DNN compression and provide examples using TensorNetworks library on top of a Tensor Flow.
Justin has MSc in Cognitive Science with focus on AI (University of Osnabrueck, Germany) and in PhD Candidate (Radboud University).
Automated food checkout with machine vision will be the future of canteens. Our solution of a food recognition deep learning framework is designed to scale for hundreds of canteens and restaurants. Learnings gathered from our team in Kyiv and operations in German canteens will be shared.
Bachelor of Science in Applied Physics. Graduated Taras Shevchenko National University of Kyiv, Radiophysics Department. Worked as Data Analyst in several leading Game Development companies in France, Germany and Ukraine (Ubisoft, Goodgames, Wargaming). Main qualification: marketing and product data analytics. Last year spent developing and supporting product AB-tests - which are the main topic of a presentation.
Company experience gained after split tests implementation will be presented - methodological and purely practical issues both with general assessment of this data-driven approach according to particularly Letyshops company experience.
Kostiantyn has a Master's Degree in Laser and Optoelectronic Engineering, is working on a PhD thesis, field of research is biomedical engineering, with a focus on developing a decision support systems. Data analytics is his passion. At LetyShops he works in the Partner Development department, conducts in-depth analysis and forecasting of the service users' behavior, their interests.
Presentation contains information about company organisational structure that supports Data Driven Development Method in dynamic environment with several simutaniously developed hypothesisys and several independed multidisciplinar teams working on one product.
Studied at NTUU ``Igor Sikorsky KPI``, faculty of Informatics and computer science, technical cybernetics department.
We would like to discuss challenges in hands tracking development on mobile devices.
What kind of challenges we faced
- Model criteria (сomputational budget, model size, accuracy, robustness, temporal consistency)
- Data (synthetic data, real data, combining different data)
- Product trade-off (use-case specific solution, different use-cases with examples, prior information)
We will talk about problems in detail and propose possible solutions.
Serhii is a data scientist with deep business understanding. Worked on many ML/DL projects like: price optimization, sales forecasting, churn prediction.
Flask is not the only one!
So, you created your model in the Jupyter Notebook, what to do next? How to create an interface for it, so other people can use it as a web service?
Streamlit is an open-source library enables you to quickly turn pure Python scripts into bespoke apps without any ``app-building knowledge”. No need to write a backend, define routes and handle HTTP requests.
I`ll tell you how to create a web service for your data in hours, not days, using Streamlit.
He graduated from Shevchenko university with a bachelor’s degree, specialty physical-mathematic.
He has 20+ years in IT. The last project was about the creation of a recommendation system (front, back, ETL, DevOps).
Before that, he did analytics of blockchain projects for investment funds and private investors in cooperation with BlockScience.
He was also engaged in developing recommender systems, development of information dissemination algorithms in social networks, automatic photo retouching, traffic analytics, style transfer grids and more.
Also, he recently launched a practical online course on Object Detection: https://learnml.today
Andrey will talk about the following at the workshop:
- The base ideas behind the object detection
- The difference between different network architectures
- Techniques used in training
- Train model
Predicting financial markets with the help of mathematics and algorithms is a tempting idea that is making brightest academia minds and mediocre amateurs spending hours on searching the “Holy Graal”.
This workshop aims to give you a hands-on sneak peek into best practices in adapting financial data to “make the markets”. We will start with a “normal” machine learning baseline and step-by-step will improve data preparation, normalization, labeling, and validation in order to correspond to the harsh realities of the unpredictable financial world. You will be able to see, that the key lies not in the complexity of the algorithm, but in the profound understanding of the underlying data.
After the workshop, you will not just get practical skills with financial time series, but also will deepen your understanding of the machine learning pipelines, which can be helpful not only in finance but in healthcare, military, and other applications very sensitive to the failures.
9:00 — 10:00
10:00 — 10:35
10:40 — 10:45
10:40 — 10:45
17 elegant ways of shooting yourself in the foot during question-answering system development
CTO Dex Technologies
10:45 — 11:30
Optimization in multi-label classification and system orchestration with TFX and Kubeflow Pipelines
Senior Data Scientist Renault Digital
10:45 — 11:30
11:30 — 11:50
Animable Neural 3D Models
Head of Research NeoRender
11:50 — 12:35
How we perform productive AB tests: methodology and organization of work
Lead Data Analyst Letyshops
Product Analyst / Project Manager Letyshops
11:50 — 12:35
Bridging Quantum Computing and Machine Learning in Tensor Flow
Director of Research and Development SoftServe
12:45 — 13:30
Top Deadly Sins of Applied Machine Learning in Finance
Dr. Luis Moreira-Matias
Head of Data Science Kreditech
12:45 — 13:30
Creating web-app for DS stuff with Streamlit
Data Scientist Data Science UA
11:40 — 13:30
13:30 — 14:30
Challenges in hands tracking on mobile devices
ML Software Engineer Snap Inc
14:30 — 15:15
External Relations Expert Vodafone
14:30 — 15:15
Object tracking system using multiple NNs with an application in Sports
Senior Machine Learning Engineer SoftConstruct
15:25 — 16:10
Data rules - the impact of Data Science on business
Manager, People Advisory Services EY
Senior Consultant, People Advisory Services EY
15:25 — 16:10
16:10 — 16:30
Object Detection with Single Shot Networks
System Architect Moneyveo
14:30 — 16:20
ML models in production. Installation and configuration
16:30 — 17:15
Automating Food Recognition for Canteens with Machine Vision
16:30 — 17:15
How to Identify An Object If You Must
17:30 — 18:15
Why your state-of-the-art ML can't predict markets
Co-Founder and CTO Neurons Lab
16:30 — 18:30
Check out the FAQs to find the answers to common questions. Can't find your question answered here? Contact us.
Where and when the conference is going to be held?
The conference will be held on March 14 from 10:00 to 19:00 in the Oasis concert hall. Kyiv, street. Lipkovskogo, 1A.
The registration starts at 9:00.
The nearest metro station is Vokzalna.
Is there any ticket discount?
We provide a 25% ticket discount for students and teachers
In order to get a discount promo code, send a photo of a student card or a document confirming that you are working as a teacher to the email@example.com. We will send you the promo code, that has to be applied when buying the ticket.
5% — from 2 tickets, 7% — from 3 tickets, 10%— from 5 tickets. Choose the desired number of tickets on the ticket sales page. The discount will apply automatically.
Please note that discount are not applied to ‘First 50’ price.
How to get an invoice for the ticket purchase?
To purchase tickets with cashless payment, send an e-mail to
firstname.lastname@example.org with the necessary information:
– The legal name of the company
– Personal info to create a ticket (name, last name, phone, position, mail)
– Requisites and number of tickets
5% — from 2 tickets, 7% — from 3 tickets, 10%— from 5 tickets.
25% ticket discount for students and teachers.
How can I get information about the topic and speakers?
You can learn more about the subject and the biography by clicking on the photo of the speaker.
Workshops. Which software to download?
Pre-register for workshops is not required. You can find out the theme of the workshop and needed software by clicking on the photo of the speaker. There will be 2 workshops at the conference.
To participate, you must have your own laptop.
Coffee breaks and lunch
During the conference there will be 2 coffee breaks and a lunch with vegetarian and meat meals options.