September 22-23, 2021
We add a learning dimension to our annual Techsylvania experience by inviting experts from leading companies to host technical workshops.
Techsylvania 2021 is going back to tech, with a series of workshops dedicated to engineers and their skill-set enhancement. During the two days, you can select from our workshop sessions featuring a wide array of topics, focused only on cutting-edge areas of technology.
Accessible only for ticket holders
Capacity of up to 100 people/ session
Addressing hot issues of the 21st-century
From idea to RPA
Interested to learn more about how we automate trading processes within Paddy Power Betfair and discover how you can use RPA in your personal life or your business?
Join our workshop and get ready to plunge into the fascinating world of RPA!
You will have the chance to understand how one simple idea can become a successful RPA project, as we go through a full automation flow, in real time, together with you.
(Betfair Romania Development)
Alex Maleș (Starfish Technologies)
Coding the Magic Internet Money – A Blockchain Programming Workshop Beyond The Market Hype
Let’s dive into the implementation of cryptocurrency tokens and NFTs! During this workshop, we’ll do a hands-on exploration of the most popular blockchain programming language – Solidity. We’ll write and run code on the mainnet of one of the supporting networks – the likes of Ethereum, Polygon and Avalanche. No matter the languages that you are proficient in, Solidity has an easy to understand syntax so it is going to be a breeze to follow.
First we’ll do a quick walk through the generic blockchain technology concepts in order to understand how the infrastructure works before we run code on it. Then we will code our own tokens in Solidity.
See you at the workshop!
Enabling user-generated video recording in cross-platform applications
The workshop aim is to show you how easily you can enable your platform, service or mobile app with user-generated video and audio. We will show you how few simple steps is all it takes for someone without a background in coding. By going to two pages on our website you can copy and then paste everything that you need. Perfect for anyone looking for a quick POC.
Of course, it would not be a workshop if all you had to do is copy paste, so we aim to also show you how you can utilize Ziggeo’s API even further. We will show some simple, quick examples yet you will see that there are many things one can do.
The main problems that we will help you address with Ziggeo, which are connected to video and audio implementation are:
- Worry free scalability and reliability
- Cross platform support
- Intuitive Design
- Automation of cumbersome processes such as moderation through use of AI
- Checking off various certification and compliance requirements
We will also quickly touch on how you can win more contracts with customers adding Ziggeo through our simple certification program.
Our goal is that by the end of the workshop you will see and know about our various server side, mobile and frontend SDKs. It is no longer a question of what to use on one platform and what to use on another. Just grab our SDK for your platforms and start hacking away with similar, cross platform coding experience.
AI-Powered Map Making at Grab
Grab.com is Southeast Asia’s leading super-app that provides everyday services such as mobility, deliveries (food, packages, groceries), mobile payments, and financial services to millions of Southeast Asians.
These services rely heavily on accurate maps. As these are not always readily available in Southeast Asia, we decided to make them ourselves using a suite of in-house-built AI-powered software tools that we constantly keep refining. Our software engineers and data scientists based in Grab’s Cluj R&D center are specialists in software-based map-making who closely collaborate with their colleagues in Singapore and Beijing to build the best and freshest maps for the region. During this session, we will be deep-diving into both software engineering and machine learning/computer vision aspects and reveal some of the techniques that we’ve been developing.
Hannes Kruppa, Head of Data Science, Machine Learning & Geo at Grab.
Adrian Margin, Senior Engineering Manager, Machine Learning at Grab.
Yuchen Fama, Director of AI Product at Clarifai
Building a High Performance AI-Powered Applications
Deep learning and computer vision are helping enterprises organize and manage their unstructured image, video, text, and audio assets. Using AI, metadata can be added to assets significantly faster and more accurately than humans would be able to do on their own. This enables companies to search and find assets faster and allows them to scale to any size.
In this workshop, learn how to integrate AI-automated metadata tagging to minimize manual processes and improve workforce productivity 100x. See how to design, train, build, deploy and scale high-performance AI-powered applications. With a single AI platform, you can build and train custom AI models at scale and set up workflows to manage your metadata generation process. Learn how to:
- Define a data taxonomy and baseline metrics
- Jumpstart your project with pre-built models
- Automate data labeling
- Train and deploy your model
- Evaluate model performance
- Improve your model with active learning
Fitbit Health Apps for the digital age
Want to learn how to build wearable health apps that can reach millions of users? In this workshop, we’ll be covering how the Fitbit Development SDK works and how developers can build rich apps focused on health data and wellbeing. We’ll deep dive with a case study of one of the health apps that we offer to our users.
Sorin Ruse, Senior Software Engineer, FDP Test Lead
Catalin Ghenea, Software Engineer, FDP SDK
Rohan Ramanath, Sr. Staff Engineer, Machine Learning at LinkedIn
Fast Incremental Learning on Data Streams
One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generated data streams, machine learning methods perform a balancing act between model complexity, training time, and computational costs. Furthermore, when model freshness is critical, the training of models becomes time-constrained. Parallelized batch offline training, although horizontally scalable, is often not time-considerate or cost-effective. In this paper, we propose Lambda Learner, a new framework for training models by incremental updates in response to mini-batches from data streams. We show that the resulting model of our framework closely estimates a periodically updated model trained on offline data and outperforms it when model updates are time-sensitive. We provide theoretical proof that the incremental learning updates improve the loss function over a stale batch model. We present a large-scale deployment on the sponsored content platform for a large social network, serving hundreds of millions of users across different channels (e.g., desktop, mobile). We address challenges and complexities from both algorithms and infrastructure perspectives and illustrate the system details for computation, storage, and streaming production of training data.
Get your Ticket Now
Previous Workshop Hosts
Senior Software Developer at Macadamian
Senior Computer Engineer at Bosch Engineering Centre
Previous workshop topics