Talk

Building a Scalable Data Science & Machine Learning cloud using Kubernetes

Tagline: A real-life story of architecting & building a cloud-native data science platform using Kubernetes.
A growing team of data scientists was looking for a simple, flexible, scalable and a secure way of migrating to the cloud as the on-prem data center started becoming a bottleneck. Kubernetes, which enables applications to run on a variety of private and public clouds, along with an ever-growing feature set, matched most of the team’s requirements.

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Machine Learning Engineering

When learning about machine learning methods, much effort is put into the fun part i.e. training and tweaking the models to improve upon your favorite performance metrics.
But when the dust settles all these toys need to be working in your production environment and you want to have as little issues with them as possible.

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The Future of Traditional Shopping Driven by Customer Centric Approaches

Artificial Intelligence gives retailers a great opportunity to evolve from point of sale to point of experience, convenience and omnichannel. In the coming years e.g., it will be easy to use bots for purchases perceived as boring and monotonous. As a consequence, offline retailers will have to be customer obsessed, keeping them engaged and loyal to their products if they don’t want to fall into an only-transactional buying category.

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A Game Theory Approach for Data Driven Business Decisions: Use Case in Portfolio Optimization

Companies have a lot of useful information obtained from data that can be transformed into analytical models ready to use in business decision processes. 
In this session, we will explore how this information, data and models, can be adapted and enriched in order to include in these company’s decision processes the existence of other players in the market, especially the competitors.

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Predicting Hotel Cancellations with Machine Learning

Hotel cancellations can cause issues for many businesses in the industry. Not only do cancellations result in lost revenue, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.
This session explores how machine learning techniques can be used to predict hotel cancellations. Firstly, data manipulation techniques with pandas are employed to effectively process over 20,000 customer entries. 

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Stream Processing for Analysts with Flink and Nussknacker

Analyzing and gaining insights from large amounts of data is one thing. Doing it in real time is a whole different business.
There quite a few advanced stream processing engines, Apache Flink is one the most widely used. However, designing, testing and deploying streaming jobs usually demand development skills – it’s not so easy for analysts or business people.

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Overview of Generative Adversarial Networks (GANs)

Until recently, generative modeling of any kind has had limited success. But now that Generative Adversarial Networks (GANs) have recently reached few tremendous milestones (and truly exponential growth in the interest in this technology), we are now closer to a general purpose framework for generating new data.
Now GANs can achieve a variety of applications such as synthesizing full-HD synthetic faces, to semi-supervised learning as well as defending and mastering adversarial examples, we can discuss them in this talk.

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Actionable Insights with Real-time Streaming Analytics of Customer Reviews

While analyzing structured data (even tremendous amounts of it) is a solved mystery nowadays, retrieving actionable insights from unstructured data (i.e. text) is the new challenge to be met. This talk even goes one step further and places this challenge in a streaming data setting. A reference architecture that works across industries will be shown to illustrate how to process text immediately after being written, how to analyze it, how to gather its meaning, and eventually visualize the results to provide actionable insights. 

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