Applied Machine Learning
Advances in compute power and digital communications have allowed organizations to embed machine learning and artificial intelligence in more applications. In this track, we’ll hear from experts on the successes they have seen from the processes and technologies they adopted while exploring best practices to help your teams leverage machine learning to improve your application experiences.
Bots & Language Processing
Large language models may garner headlines, but they are only the latest in a long line of innovations associated with natural language processing. In this track, we’ll explore the full breadth of the NLP and chat bot space to discuss the techniques that work best while delivering real value both to organizations and their end users.
Data Science & Predictive Models
Data science enables a company to utilize algorithms and predictive models to draw insights and forecasts from their data. What are the cutting-edge technologies and best practices for implementing data science initiatives in your company, and what types of predictive models prove the most useful?
Deep AI Learning & Neural Networks
Many of the most impressive applications of AI/ML technology has come from “deep learning” models that leverage complex math and computing techniques to turn input data into meaningful predictions. In this track, we’ll do a deep dive into these techniques to explore their associated benefits and challenges while keeping an eye toward what’s coming next.
Generative AI & LLMs
The ability for AI models to create new text, images, and similar content from trained data seems almost like magic. As research advances, this creative ability continues to improve, bringing both new opportunities and new concerns as it matures. In this track, we’ll explore the realm of generative AI models, discuss their strengths and where improvement is still needed, and explore ways to integrate these models safely and effectively into your organization’s products and processes.
MLOps & AIOps
Managing artificial intelligence and machine learning projects often comes down to managing the enormous amounts of data and compute required to build functional models. In this track, we’ll explore the technologies, processes, and best practices that make managing your AI/ML pipelines easier while improving your team’s performance.
Tensorflow, PyTorch & Open Source Frameworks
Exciting new models and approaches to AI/ML regularly emerge from the researchers focused on them, but their power can only be unlocked when implemented in end user applications. In this track, we’ll explore the AI/ML frameworks that make these models accessible to developers and discuss best practices for managing and maintaining applications built upon them as the rapid pace of innovation continues.
AI Security, Ethics, Governance & Compliance
The growing power of AI is impressive, but researchers continue to place strong guardrails around their models to keep them from being used nefariously. In this track, we’ll explore the security topics relevant for AI researchers and implementors, surfacing best practices and processes that will keep your data, your users, and your AI models and system safe from bad actors and inadvertent activities.