Traditional architecture practices have served us well for many years, but weren’t developed with an understanding of the demands of the era of DevOps, agility, cloud, and microservices. However, today, effective architecture is more important than ever. This is even more relevant with the focus on Continuous Delivery for Faster Innovation.
The demands of modern software engineering mean that we need updated architecture practices that allow organizations to effectively manage complex, conflicting and cross-cutting concerns such as resilience, security, and technical coherence, and meet the fast-changing needs of complex groups of stakeholders.
Continuous Architecture’s objective is to update the discipline’s classic practices for today’s environments, software development contexts, and applications. In this talk we will focus on the core six principles of Continuous Architecture and look into the essential activities of architecture.

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This talk takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This session gives you a head start. Following are the takeaways from this session;

• What MLOps is, the motivation behind it, and why it’s the next frontier in applied machine learning
• Learn how to harness cloud technologies like AWS AppRunner to deploy and monitor machine learning models in production
• Summary of use cases and challenges in MLOps, and how to begin the MLOps journey in your organization

Software is incredibly hard to secure because it's a black box. We've spent decades struggling to verify properties of software from the outside by analyzing the source code, scanning, fuzzing, pentesting, etc... The goal of "security observability" is to expose exactly what's going on inside the box while it's running. Analyzing a running application has speed, accuracy, coverage, and scalability advantages that change the way Dev, Sec, and Ops communicate and work together. In this talk, you'll learn how to use the free and open source Java Observability Toolkit (JOT) project to easily create your own powerful runtime instrumentation without coding. You can use JOT to analyze security defenses, identify complex vulnerabilities, create custom sandboxes, and enforce policy at runtime. Ultimately, security observability enables DevSecOps to work together in harmony, so you can focus on delivering value at high velocity.

In an organisation, executives are the key players who manage the scope of DevOps implementation into their organisation by identifying, selecting and prioritising the vital elements for success. In this panel discussion, let’s delve into the experiences of technology leaders of various enterprises to learn about