Discover what happened at past events:

June 21: The Challenges of Machine Learning Deployment - Veselina Staneva

We'll be speaking with Veselina Staneva about how to grow a product mindset in MLOps, the challenges around deployments, delivering business value, speed of iteration, and how increasing it is mission critical.

Our guest for this talk is Veselina Staneva, Co-founder & Head of Product at TeachableHub.

May 25th: The Modern Data Stack for Machine Learning Pipelines

Join this live conversation with Benjamin Rogojan, aka “Seattle Data Guy” about the modern data engineering and machine learning stack.

We’ll discuss what the modern data stack for machine learning looks like and how to get started with data engineering to build your own machine learning pipelines.

Benjamin Rogojan aka, “Seattle Data Guy” is a data science & data engineering consultant + content creator with years of experience working in healthcare and FAANG companies as a data engineer.

May 17th: Real World Applications of Generative Deep Learning & NeRFs

Join this live conversation with Jay Silvas about real world applications of generative deep learning and neural radiance fields (NeRFs) for 3D object creation.

We’ll discuss interesting use cases of generative deep learning, an exciting AI field that allows computers to make original designs, pictures, poems, music, and more!

We’ll also dive into applications of neural radiance fields (NeRFs), a specific type of generative deep learning that create complex 3D models based on a relatively small dataset of images.

April 15: Investing in Preventative Health for Data

As data volume and complexity grow in an organization, sound decision making and high-quality AI products depend on having a robust approach to maintaining data health. In this talk, Anna Swigart will outline a framework for how to invest in preventative health for data, including choosing the right “data vitals” to measure, how to keep bad data from propagating through systems, early detection of anomalies using ML-driven tools like Luminaire, and being systematic about cleaning up data clutter.

March 10: AI Incident Response - What to Do When AI Fails

AI and ML have at least one thing in common with traditional software systems: they all fail. AI failures might consist of discriminatory behavior, of privacy violations, or even security breaches that can lead to lawsuits, regulatory fines and more. What can organizations do to avoid these pitfalls? In this talk Patrick Hall will outline a new approach to “incident response” specifically tailored to AI and it will present a free and open sample AI incident response plan. Participants will leave understanding when and why AI creates liability for the organizations that employ it, and how organizations should react when their AI causes major problems.

February 18: ML + UX - Building ML Products for People

Can you do UX Research on machine learning systems? Can you get feedback from real users before the AI has been built? Can you even test an ML system before you have a production-ready model? Yes, yes, and yes! In this talk, Michelle Carney will share about how she combines her background in machine learning with her expertise in UX - including the MLUX meetup she organizes, her favorite resource the People + AI Research Guidebook, and a case study on her process of doing UXR for ML.

November 17: Deploying highly optimized models to any hardware

Let's face it - deploying trained ML models into production is a lot harder than it should be. Join us with Jason Knight and Chris Hoge from OctoML for a conversation on how Apache TVM, an open-source compiler stack, addresses the challenge of deploying models to all kinds of hardware.