Agile + DevOps USA 2024 - IT Operations

Executive leaders across the globe have been asking a relatively simple yet profound question: Can we leverage generative AI to transform our business, enterprise, or industry? For software-based companies, focus has either been on differentiating their product and service offerings using this new technology. But how about leveraging generative AI to improve team productivity and efficiency? This may be possible but how do you measure its success? What are some of the key use cases within business analysis, development, and testing that software teams can use? Are there any pitfalls...

Whether you are new or experienced with GitHub this class is for you! Supercharging your workflow caters to anyone who wants to enhance their agile and DevOps process with the capabilities of GitHub. GitHub has long been the premier site for open-source projects and is now turning a pivotal corner into becoming the predominant platform for all aspects of the development lifecycle. Some examples of this include; protecting company code through various GitHub Products or curating marketplace actions and workflows prior to use. This tutorial will look at how to leverage GitHub Actions (CI/CD...

Our world is filled with complexities and unknowns, and when these collide with emerging technologies, the resulting risk seems insurmountable. We accept this increased risk as a cost of using the new technology, assuming that previous lessons learned don’t apply and that a blank slate is required to gain the most benefit. However, this never turns out to be true. Time and time again, we realize that much of what we already know is still relevant to these emerging technologies, regardless of how advanced or paradigm-shifting they are. This is good news for reducing the risk in innovation...

With the rapid adoption of generative AI, more and more companies are infusing AI models and services into their products. However, many of these companies are likely to lose business and valuable revenue due to their lack of investment in MLOps. Typically, organizations developing AI systems have relied on training metrics like accuracy, precision and recall, but software quality goes beyond that. Now that the barrier to entry for AI tools is smaller, we need to set quality standards, test practices, and think about AI ethics and safety. Ensuring the quality of AI goes beyond traditional...