We’ll be speaking with Magdalena Konkiewicz from Toloka to learn about data centric AI and why it’s important for the field of machine
Read MoreAt this event we’ll be speaking with Doris Xin about the good + the bad of automated machine learning (AutoML) and how to empower data science teams to deliver insights faster.
Doris Xin is the Co-founder & CEO at Linea.ai. Doris holds a PhD in computer science from Berkeley. They have spent time as a machine learning researcher and engineer at Google, Microsoft, Databricks, and LinkedIn.
View past event on EventbriteWe'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.
View past event on EventbriteJoin 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.
View past event on EventbriteJoin 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.
View past event on EventbriteAs 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.
View past event on EventbriteAI 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.
View past event on Eventbrite