OPEN Expo Workshop Stage
Tuesday, February 8, 2022
The explosion of IoT devices and the time series data they generate has accelerated the demand for specialized IoT platforms. By 2025, there’s projected to be ~60 billion connected devices around the world. The hurdles for businesses to overcome in the next few years will be centered around collecting, munging, and storing it all from the many sources it comes from, how to manage that storage, and how to analyze it most efficiently.
In this session Sam Dillard will cover why a time series data platform should be used to meet IoT scale and interoperability needs. He will walk through the core concepts of time series databases, share use cases and give us an overview of the InfluxDB platform.
There are no shortage of API metrics you could track, but how do you aligned to business outcomes. This workshop takes a deep dive on how to align metrics to three key goals: Adoption, Engagement, and Retention. Then, we'll discuss changes you can make to your developer experience for improving these areas.
Testing in production used to be a joke. In fact, it was a popular "Most interesting man in the world" meme. But as life often imitates art, this meme has become reality. As it turns out, the best tests to learn from are the ones that match production. So when looking for feature flag solutions, developers and software delivery teams find themselves looking for ways to test their code and deployments in prod!
In this talk we will dip our toes into the world of feature flags. We'll begin with an overview of what feature flags are, how to think about them, and why both engineers and business users find them valuable. In addition, you’ll also learn about how to get started with feature flags, and the key things to look out for once you "Do it live!" .
Secure software development isn’t always a top concern to the business unless you are in a highly regulated industry. Today, time to market is often more important than security, increasing the value of the product that you sell with continuous improvement and quick software releases. To create and maintain a lead on the competition, you have to be really good at Agile and DevOps.
A potential scenario: the security team has called an emergency meeting. A new vulnerability has been publicly disclosed that impacts not only your software, but your company and your customers. Will the required remediation take hours or even weeks to complete? It depends on your preparedness.
To improve your readiness and reduce impact, we will look at tips and actions you can take now.
1. Learn more about the scope of the mess that was created by the Log4j CVE.
2. Why most companies struggled to address it quickly.
3. What steps you can take now to be ready for the next one.
Traditional monitoring and observability platforms continue to support the same approach: DevOps and SRE teams must centralize logs, metrics, and traces before they can start to analyze them. Faced with exploding data volumes, teams dependent on these platforms are left trying to predict which systems and datasets to monitor and centralize. What doesn’t meet the bar gets neglected or discarded altogether. You shouldn’t have to compromise data visibility to stay within budget. In this session, Edge Delta CEO and Co-Founder, Ozan Unlu will break down Edge Observability -- a novel approach to observability that aims to solve this issue. You will learn how DevOps and SRE teams can maximize visibility, optimize costs, and respond to issues orders of magnitude faster.
A well-crafted container or kubernetes avoids using excessive privileges, shipping unused packages, leaking credentials, and will expose a minimal attack surface. By removing known risks in advance, you’ll reduce security management and operational overhead; however, not everything can be known and prevented in advance. You cannot forget about security since the container is running.
Join this session to gain clear direction on how to:
- Image build and apply Dockerfile best practices
- Reduce the attack surface and optimize size for distribution using multistage builds
- Manage threats and vulnerabilities, like log4j
Cricket, a game of bat and ball is one of the most popular game and played in varied formats(I. Its a game of numbers with each match generating plethora of data about players and match. This data is used by analysts and data scientists to uncover meaningful insights and forecast about matches and players performance. In this session, I'll be performing some analytics and prediction on the cricket data using Microsoft ML.Net framework and C#.
Wednesday, February 9, 2022
Observability has never been more important: kubernetes and distributed architectures are necessary, but they make it harder and harder to answer basic questions about system behavior. The conventional wisdom claims that Metrics, Logging and Tracing are “the three pillars” of observability… yet software organizations check these three boxes and are still grasping at straws during emergencies.
In this session, we’ll examine how we got ourselves into this predicament, and how to get ourselves out of it. We will talk briefly about the theory and then illustrate more elegant and modern approaches to common observability problems using live demos.
Good user experience requires a well performing frontend application. Code observability on a frontend application—to understand errors and their relevancy, performance of transactions, and Web Vitals to quantify website quality—is complex. By attending this session, you'll learn more about the tools that are available to aggregate and organize relevant frontend data to provide necessary visibility on errors and performance to keep users engaged.
We don't usually set out to write a monolith...but it happens. With changes over time, and limited resources to refactor, our application can turn into a "legacy monolith" that runs for years, and years, and that we all dread working on! In this session, learn about the AWS Microservice Extractor for .NET and how it can help you identify and extract parts of your application into services. Transforming your monolithic applications into smaller, independent services makes them easier to scale, more efficient to operate, and faster to develop, accelerating time to market for new features. Then go a step further to re-platforming to ASP.NET Core running on Linux, by adding to your tool chain the Porting Assistant for .NET. Come and learn how!
Just a few years ago every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon, rolled their own AI/ML tech stack from scratch. Fast forward to today and we have a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? In order for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast.
Join us in this presentation as we cover:
What are the components for true MLOps
How do teams begin their journey into AI and Machine Learning
Why teams should take a data first approach to ML
Context is a crucial component of moving from Monitoring to Observability. Attaching rich context to application tracing allows us to answer fundamental questions required for true Observability, like "What changed?" In this talk, we'll discuss what context is, how it's applied, what problems it solves, and what challenges it presents.
Talent is becoming the biggest barrier to growth for tech companies, seeing an attrition rate 3x higher than before the pandemic.In this talk, we will address the challenges and how Turing was able to build an AI-powered platform that has attracted 1M+ engineers across the globe.