Speaker: Daniel Whitenack

Data Scientist @SILintl

Daniel Whitenack is a Ph.D. trained data scientist working with SIL International on NLP and speech technology for local languages in emerging markets. He has more than ten years of experience developing and deploying machine learning models at scale. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (Applied Machine Learning Days, O’Reilly AI, QCon AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.

Find Daniel Whitenack at:

Workshop

Practical AI with Python and PyTorch

Most companies are considering, or are in the process of, integrating artificial intelligence (AI), machine learning (ML), or predictive analytics into their business. Huge value can be gained from leveraging your organization’s existing data to optimize logistics, make recommendations to users, or predict anomalous behavior. AI/ML models have proven to be well suited to these and many other problems.

All that being said, kicking off an AI/ML initiative can be an intimidating process, because, among other things:

  • Your more traditional software engineers are unfamiliar with the theoretical foundations, languages and frameworks utilized in AI applications.
  • Your infrastructure team(s) do not understand where and how AI applications can or should interface with existing systems.
  • You are not sure if the AI methods that are much hyped on social media could actually bring value based on your data and goals.

In this workshop, we will take the hype of AI/ML and make it practical for real world business use cases. We will dig into the theory and frameworks that are driving the latest advances in AI, and we will spend practical, hands-on time learning how to train and utilize/integrate AI models. 

Topics covered include:

  • Fundamentals of AI
  • Types of AI modeling
  • Model development workflows
  • AI development with Python and PyTorch

Level

Level Beginner

Share

Workshop

AI at the Edge for IoT Applications

Companies that want to be both AI forward and move into the IoT space must wrestle with running models on low power edge devices. Moreover, as AI models become ubiquitous, companies are facing increased pressure to prioritize privacy and maintain integrity with respect to the handling of user data. These pressures naturally lead to AI applications that run directly on edge devices or limit centralized data processing. 

In this workshop, you'll learn essential methods and strategies for building privacy conserving AI applications that can run on edge devices. The presenter, Daniel Whitenack, will help you understand the devices and challenges unique to "the edge," and then he will walk you through key technologies including Intel's OpenVINO toolkit. In the end, you will be able to help your company develop a strategy for AI at the Edge and implement that strategy with Python and OpenVINO.

Level

Level Beginner

Share

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.