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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.

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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.

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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.

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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.

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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.

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It's extremely difficult to anticipate all the ways a user might want to interact with an open-ended conversational interface. The good news is that you don't have to. Conversational driven development is a framework that can be used to both improve the developer experience and create more inclusive, robust conversational interfaces. Join us with Rachael Tatman, Senior Developer Advocate @ Rasa, where we'll walk through the conversational driven development process and its benefits and drawbacks!

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Curating a dataset for ML applications involves decisions that are prone to subjectivity, which poses both ethical and technical issues. After creating datasets and running them through a model, there aren't many best practices on error analysis to better understand systematic behavior in NLP. Join us for a conversation on strategies for being a critical consumer and producer of datasets and operationalizing linguistically informed error analysis in various NLP applications!

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Any robust ML/AI application should begin with reliable & reproducible data pipelines. Yet, when speaking with practitioners they cite ML pipelines to be one of the biggest infrastructural challenges of an AI project. What are the best practices in building ML pipelines? What does the open source ecosystem offer today? What is your current wishlist when it comes to ML pipelines? All of these questions and more will be discussed in an exciting talk presented by a Microsoft's Head of Open Source ML & co-founder of Kubeflow.

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Explainable ML has been one of the hottest topics in the ML community over the years. There are countless articles on methods and techniques that could be used to enable explainability. How are these techniques actually used in deployed systems? What are the gaps between existing techniques and the needs of production systems? Join us for two fantastic talks that bring forward extensive research conducted by Partnership on AI and University of Cambridge on Explainable ML's reality & roadmap.

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