Data Analytics Online Summit (Time Zone – EST)

Registration / Conference Overview

The talk will focus on how implementing Robotic Process Automation(RPA) in the sector of finance is optimizing the manual operational processes, while offering effective protection against financial cyber threats. We'll then talk about how RPA is also beneficial for data management and is disrupting financial services with infused Artificial Intelligence(AI).

Key takeaways from my session:
In this session, we will walk through the process of succeeding in the analytics journey. We will examine the best approach to building a corporate analytics function and a high performing analytics team. We will describe and discuss the Artisanal and Modular approaches to hiring and building a team. We will also examine how to evolve from the Artisanal and/or Modular model to a Hybrid model. We will examine how to manage a high performing analytics team to deliver analytical models and applications that are implemented and used in daily operations to improve organizational efficiency and effectiveness. Be ready to ask questions, this will be an interactive and open discussion.
Format can be any of the below:
· 45 minutes Presentation with real life examples

Break

Understanding the challenges in applying AI at scale for online decision making, and the opportunities for improved products and architectures that lie in solving them.

There is a ton of resources available on how to train machine-learned models, but how do you make use of them in real solutions once they are trained? If you need to evaluate models over big data sets and scale to thousands or millions of evaluations per second, such as in online recommender and ad systems, this is a very hard challenge. A few of the few biggest internet companies in the world has proprietary solutions for solving this problem, but until recently it has been out of reach for anybody else.

This talk will explain why this is a hard problem, show some of the things that become possible once a solution is available, and describe how we use the open source Vespa.ai platform to solve this problem in some real use cases including the world's third largest ad network.

Bert and other deep learning technologies need to be adapted to work for long document classification

The space race was a EEUU – Soviet Union competition to conquer the space. This competence helped to develop space technology in an incredible manner, developing other derivative technologies as a side effect.
This race was full of success in both sides, achieving goals that seemed impossible in record time.
From this space race we can learn some lessons that we can apply to our Machine Learning projects to have a bigger success rate in a limited amount of time.

As more and more AI/ML models being developed, it becomes a critical topic on how to manage and governance your models. Our solution is to provide a centralized platform with one simple user inteface for data exploration, automated ML, model deployment, and drfit tracking; democrotize the access to data and enable the company to quickly operationalize the data projects.

Today many industries have to process wast amounts of information daily. This is especially true for online advertisement, where companies need to crunch through terabytes of data daily. In this talk, we'll discuss the typical approaches for handling a lot of data fast and deploying them to production.