October, 5, 19:00
Machine Learning and Data Management Challenges
Language: UA & RU
Online
for Venture Capital Investment Optimization in Big Data World
or visit Ciklum Kyiv office, Amosova 12
What’s on:
01

The modeling target definition in conditions of uncertainty with business aims
02

Data set preprocessing issues for the data sources zoo
03

The modeling approach: the use of tree-based LGBM Classifier and K-Fold cross-validation
04

Data Architecture design and implementation with AWS technologies
We will use a real case of the venture capital investment company to talk about:


  • defining the modeling target in conditions of uncertainty with a business target

  • choosing the data management platform for a data set of millions of records and hundreds of features

  • selecting the probability estimation model for the company performance optimization.
The company operates millions of prospective startups and tries to find a worthy investment possibility.

While we were working on the project, our goal was to develop an intelligent machine learning platform and a set of models which would utilize a large amount of data to arrange prospective companies according to their investment attractiveness.

We contribute an approach of applying tree-based learning algorithms to uncorrelated, noisy, and imbalanced datasets for financial data modeling. The details of implementation will be covered during the session. This includes data platform architecture in the AWS cloud.
About the speaker:
Vitalii Bondarenko
Head of Data & Analytics Centre of Excellence at Ciklum
Vitalii has been designing data-centric systems for the last 20 years and has gained tremendous experience in OLTP, DW, and BI/ML platforms. During the previous five years, Vitalii was involved as a Solution Architect in Cloud Data projects and has adopted different innovative approaches for Fast Data Processing and Machine Learning. Now, Vitalii's responsibility is to lead the Data and Analytics Centre of Excellence and build expertise in cloud data services.
Denys Osipenko
Head of Data Science Unit at Ciklum at Ciklum
Denys has 15-year experience in Data Science. He was involved in Credit Scoring and Decision Making systems development for Retail Banking and Fintech, as well as worked as a Risk Modelling Manager. Denys has a PhD in Management Science from the University of Edinburgh. The areas of his interests are Customer Behaviour investigation, financial modeling, Decision-Making processes optimization, and the use of Big Data for Clients Insights. Now Denys is the Head of the Data Science Unit in Ciklum. He is working on developing excellent expertise in Data Analytics to integrate three main competencies in Machine Learning and Predictive Modelling, Computer Vision and Deep Learning, and Business Intelligence.
Oleksandra Boguslavska
Moderator of the event, CEO & Founder Data Science UA
Oleksandra Bohuslavska is a CEO/ Founder Data Science UA Since 2016, Oleksandra has been actively building and developing the Data Science Community in Ukraine. Oleksandra is continuing to grow an ecosystem in Ukraine that unites the best AI engineers and the most progressive companies.
Sign up if you are:
Data Engineer, Data Scientist, Data Analyst, Data Architect;
1
Data enthusiast
2
a professional interested in Big Data and its application in business
3
Registration form.
Online:
We will email the Zoom link one day before the webinar to everyone registered

Offline:
The number of seats is limited. We will email an invitation on the 1st of October to confirm your attendance in the Ciklum Kyiv office on Amosova 12
Would you like to participate online or offline?
Your personal data may be processed by Ciklum Group * subsidiaries as the owner in accordance with the GDPR or other relevant legislation. Please read the Privacy Notice for more information. If you have any questions about your rights as a personal data subject or if you would like to withdraw your consent to the processing, please send a request to data.privacy@ciklum.com. Placing a mark in the box, you consent to the processing of your personal data.