AI Con USA 2025 - Machine Learning
Sunday, June 8
Fundamentals of AI—ICAgile Certification (ICP-FAI)
GitHub Copilot Certification Bootcamp
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...
Tuesday, June 10
A Quality Engineering Introduction to AI and Machine Learning
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,...
This Is Our Agent, We Make the Call
NewAgents! Finally, a term that might be even more over-hyped than AI! But what exactly are Agents, and more importantly, how do you use them? Where do Agents fit within the overall AI toolkit? What capabilities do they add, and what tasks can they help us perform? Dona and Jeremiah will help you gain a solid understanding of Agents and the fundamental components of an Agent-based AI system. You’ll learn key design principles for Agents, as well as when to leverage them and scenarios where they may not be the best choice. We'll cover how to identify business problems that are well-suited for...
Wednesday, June 11
Moving from Reproducible Data Science to the World of LLM, GPT, RAG, and Hallucination
We are moving from the world of reproducible data science (machine learning, area under the curve, a/b testing, model selection, model testing, model fitting and model drift to find the best way to run a data science organization) to being faced with the "Easy Button." This "Easy Button" is often wrong, has poor data protocols, and tries to please; without factual basis. Join Bob to explore ways to make the world better in the bridge from science to the new world of utterances. This session will include examples and experiences from IBM Watson, AI at GE Healthcare, AI at Microsoft, and...
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...
Thursday, June 12
Shadow Vulnerabilities in AI/ML Data Stacks - What You Don’t Know CAN Hurt You
The adoption of open-source AI software introduces a new family of vulnerabilities to organizations. Some components in AI, like model serving, include Remote Code Execution (RCE) by design, like when loading pre-trained models from external sources. Traditional SCA and SAST approaches are not built for the AI ecosystem leaving a huge & insecure attack surface. The irony is that in the AI ecosystem, security issues such as remote code execution are actually a feature and not a bug, often specified explicitly in the docs, which most devs don’t read. AI models are often downloaded from...