Thursday 20 May 2021
THEMES: APPS / INFRA
5,5h packed with Amazing Speeches, a Panel and Demo.
Welcome and Introduction
Observability in the Cloud and the MELT stack
What has changed in monitoring as we have moved to the Cloud?
How have our observability needs evolved while moving from monolithic to microservice architectures?
We’ll start by understanding the different application lifecycle and data flows in cloud-native applications, in order to understand the changing needs of modern observability.
We’ll go through the 4 basic telemetry types, MELT (metrics, events, logs, traces), and how these can be implemented with modern tools (CNCF) on distributed systems.
Finally, we’ll put it all together to see how we can gain total visibility in our applications through distributed tracing and synthetic monitoring.
Throughout the presentation, we’ll be using examples using open-source tools (Elastic, Prometheus/Thanos etc), as well as SaaS solutions (like New Relic).
Gold Sponsors "Nokia", "Fuelics" joint demo
Automation and AI/ML in Fixed Networks
To answer the massive increase in internet traffic, fixed networks are rapidly evolving and tend to become extremely complex to manage manually.
Hopefully, many network functions can be virtualized and abstracted, opening the door for a higher level of automation.
In this context, AI/ML can help in making networks much more autonomous, by combining massive telemetry with closed-loop control and anomaly detection.
This presentation will shed some light on how AI/ML is introduced at the heart of a network controller, as well as how it is used internally to improve the design of network functions.
Silver Sponsor "Signal" speech
Panel: Reversing Brain-Drain
During the panel, we'll discuss how we can minimize the brain drain from Greece, or even better reverse it by creating an environment for people to come back.
• Creating high-skilled jobs in Greece
• The environment needed for returning back
• Build local, go global
• Greece's role in remote work and the pandemic era
Silver Sponsor "Intralot": Azure Modern Architecture on Large-scale Data Centers
Agility in the world of Data Science, Machine Learning & AI
Great leaps are being made in the development of Machine Learning (Artificial Intelligence) models that are capable of reading, identifying, and interpreting patterns in data; be they images, videos, words, or numbers.
The systems that achieve this are complex by nature, and so are the teams that build them; typically comprising the blended skills of Data Engineers, Mathematicians and Data Scientists.
Together these people are tasked with developing systems that consume data, process it and generate valuable insights that are then presented to their human operators to assist them in their work.
By definition these projects are complex and often experimental by nature, which means that Agile Practices should be ideally suited.
But which ones to use and why?
Why do Data Scientists, sometimes, rebel against being Agile?
What do we need to do differently when working with experimental Data Science models, as opposed to established ones?
Where does model training fit in?
And how do we estimate tasks that are both simple and time consuming?
In this session we will look at the practicalities of applying Agile Thinking and Frameworks to projects led by Data Science