In this session, Antia Fernandez will talk about different applications of artificial intelligence for the food sector Gradiant clients. As an introduction type of data from the company (sensors, business and people) and what we do in a generic way to extract more business value will be presented.

While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle to follow up with the amount of data generated in terms of efficient processing and anomaly detection. With the rapid growth of web attacks, anomaly detection becomes a necessary part of the management of modern large-scale distributed web applications.

Sagar Kurandwad

AI Redefined, Canada

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.

In this session we will discuss DevOps and the transition to DevSecOps, and what we can do better (as an industry) when it comes to embedding security in DataOps.

Machine learning is a catalyst for productivity growth. In the near future, many current jobs and tasks will be performed totally by machine learning and Artificial Intelligence algorithms or with the usage of them. According to PWC, machine learning in economics can increase productivity by up to 14.3% by 2030.

In this talk, we will cover our experience liberating the data in the mainframe with a hybrid serverless/containerized solution built on AWS. We will dedicate a special section to performance optimizations done to reduce the cold start problems and support aggressive performance targets.

In this talk, you’ll first get a basic introduction to neural networks, the concepts behind training them, and how to build and train a DL model without needing a single line of code using the Keras integration of the KNIME Analytics Platform. Once you are familiar with the basics we will move on to convolution neural networks, for image processing.

Presentation of the first complete AI investment platform. It is based on the most innovative AI methods: most advanced neural networks (ResNet/DenseNet, LSTM, GAN autoencoders) and reinforcement learning for risk control and position sizing using the Alpha Zero approach.

Anna Warno

AI Investments, Poland

She will be talking about the challenges in building this kind of platform, switching from service-oriented data science teams to product-oriented teams, changing the mindset, biggest problems encountered, and how we managed to tackle growth, client pressure, data science, heuristics, and algorithms under the same umbrella.