AI Con USA 2024 - Tutorials

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...

All successful AI projects start and end with the data. The problem? Not everyone has the data required to build AI models in a production setting. Dataset generation has grown significantly with the rise of generative AI, making it easy for anyone to get started with training models no matter how much of your own data you bring to the table. While this sounds great, there are a ton of variables that go into this process of successfully generating data for training. How much data is needed? What models should be used to generate data? How to prompt models to generate high-variance datasets...

Much attention is given to machine learning model training and testing in the industry. While these activities are essential for producing a production-ready machine learning model, organizations face some critical business challenges that must be addressed when building and testing machine learning models. Things like the reproducibility of results, accuracy of predictions, reusability of components, and trackability of experimentation are all vital to the success of any application.  The term MLOps has emerged as a method for applying DevOps practices and automation to the machine...

The advent of Generative AI (GenAI), including Large Language Models (LLMs) and tools like ChatGPT, is not just another technological shift—it's a paradigm change, particularly in the realm of software testing. Unlike the transitions to mobile or cloud computing, GenAI introduces both unparalleled utility and disruption in software quality assurance. This session is dedicated to demystifying GenAI in software testing, distinguishing hype from reality, and providing practical tools and techniques to enhance your team's software quality while also highlighting potential pitfalls and...

In this beginner-friendly workshop you'll see how you can get started with data analytics and data science using Jupyter Notebooks. Matt will start with the basics of notebooks and then move on to using Python, Pandas, and NumPy to perform basic exploratory data analysis. See how you can use Plotly Express to create interactive charts and visuals with only a minimal amount of code. Once you've grasped the basics of understanding and visualizing the data Matt will move on to machine learning with SciKit-Learn as you train and evaluate predictive regression and classification models....