Move fast, build things… safely!

The demand for speed and innovation is everywhere. However, that enthusiasm sometimes creates conflict when it butts up against security, resiliency and reliability concerns. In some cases, the dogged hunt to increase velocity can introduce new challenges and risks (e.g., skipped process gates, insufficient testing…).
On the other hand, Formula 1 is one of the fastest and dangerous motorsports in the world. However, the safety of the drivers come always first with many regulations and checks to ensure it.
In this talk, we are going to discuss different principles followed in Formula 1 and what lessons we can learn from and adopt in the way we design, architect, ship and secure our systems.

ML in Java, YES it's possible!

When you think about building and/or deploying machine learning models, Java is not within the top 3 languages that we generally think of. Nonetheless Java has a strong community, with millions of developers using it as their main language.
With the AI buzz spiking in the past month, All developers, and especially Java developers, need to understand how to build and run apps that use ML. In this session we are going to explore what are our options, as java developers, to build, save and run machine learning models.We are also going to discuss and compare the most 3 popular frameworks (Deeplearning4J, djl and Tribuo), along with a real world example to understand capabilities and tradeoffs of each framework. We will also briefly cover JSR381, an open-source, Java-friendly API for ML, specifically visual recognition

Into the hive of eBPF!

The buzz around eBPF is growing quickly and it is rising as an essential component for observability, security, tracing and networking within the cloud native ecosystem. In fact, it is changing the way we think about operating systems by opening the doors to fully customize the Linux kernel as a platform.

In this session, we will cover the fundamentals of eBPF,its internals and core architecture. We will also delve into building real world examples and how developers can benefit from getting an inside look into their applications without code instrumentation, getting traces, analyzing the performance and identifying possibilities to improve using eBPF.

We will also cover eBPF's benefits for cloud-native environments, highlighting its expanding landscape allowing to harness eBPF power and unlock its potential.

This session will cover:
- What is ebPF, its core architecture?
- How does eBPF work?
- building and running different ebpf demo programs
- eBPF in cloud native and its growing ecosystem
- Demo of monitoring, tracing, performance optimisation in k8s cluster.
- Advantages vs disadvantages of ebpf

Continuous Profiling, the missing piece in your observability puzzle!

We’re living in an ever growing distributed world, making our services smaller, deployed and multiple container instances, deployed in different nodes and regions around the globe, making it even harder to profile our application, analyze and improve its performance.

Continuous profiling is rapidly gaining interest and growing in popularity within the cloud native community. Unlike traditional profiling strategies where data at some specific point in time, continuous profiling advocates for continuous data collection and analysis with the goal of obtaining a near real-time understanding of application performance and improving code efficiency across the entire stack.

This pragmatic talk will help you understand the ins and outs of continuous profiling. You’ll also understand how continuous profiling fits into the observability stack? What are some popular open source options ? and what are its challenges ?