AI Con USA 2025 - Data Strategy
Sunday, June 8
Certified Data & Analytics Tester (DAU-CDAT)
Monday, June 9
Getting Started with AI and Machine Learning
Are you a software professional who would like to learn to use AI and machine learning (ML), but don't know how to get started? One of the best ways to get into ML is by designing and completing small projects. Although you will ultimately need to understand the fundamentals of AI/ML, there's no reason why you can't learn foundational terms, concepts and principles as you put them into practice. Join Dionny Santiago as he introduces you to the world of applied machine learning. Dionny will guide you through a series of ML projects end-to-end, enabling you to gain experience with creating...
Beginning Data Analysis and Machine Learning with Jupyter Notebooks
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. The...
Get Your Data Ready for AI/ML
Understanding the readiness of your source data before you launch an expensive AI/ML project lets you take corrective data engineering measures that will streamline the project and give you the best probability of a successful outcome. Artificial Intelligence (AI) and Machine Learning (ML) projects can provide significant returns on investment when they are applied to narrow but difficult business problems and are supported by adequate amounts of relevant, quality data. Many such projects start with high hopes but get derailed due to fundamental problems with source data, which were...
Wednesday, June 11
Customer Churn Prediction Using MLFlow and Streamlit
This session will guide you through every stage of the machine learning process, from data preprocessing and feature engineering to model training, pipeline construction, and deployment. The session will begin by preparing and transforming data to ensure it’s ready for model training. Next, dive into building scalable ML pipelines within MLFlow, where you’ll learn how to track experiments, monitor model performance, and manage version control. These features enable a streamlined and reproducible workflow, empowering both beginner and experienced practitioners to understand and implement...