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Event

September 29: Improving Conversational AI Systems

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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October 13: Careful Considerations in NLP - Datasets and Error Analysis

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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August 6: A Practical Approach to Reliable ML Pipelines

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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July 22, 2020: Explainable ML - Reality & Roadmap

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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May 28, 2020: Operating AI (Virtual)

Building a robust model is only part of the story. What does it take to deploy it and to operate it in production? What infrastructure must be built around the model & data to ensure smooth operations? What MLOps tools are out there and how to evaluate them? If these questions are top of mind for you - it's your lucky day! Join us for two incredible talks from leaders of prominent AI-first startups, Algorithmia & Lexion, who will share their perspectives, war stories and solutions.

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May 7, 2020: Data Visualization (Virtual)

Making sense of large and complex data requires methods that integrate human judgment and domain expertise with modern data processing systems. To meet this challenge, Dominik Moritz combines methods from visualization, data management, human-computer interaction, and programming languages to enable more effective and more scalable methods for interactive data analysis and communication. Come to learn how to make sense of your data from the co-creator of Vega-Lite, contributor to Altair, Vega, JupyterLab and many others.

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February 19, 2020: Federated Learning & Privacy in AI

Privacy is top of mind for all AI practitioners. Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. The emerging field of Federated Learning offers methods and techniques that decouple the ability to do machine learning from the need to store the data in the cloud. Come learn about the potential and applications of Federated Learning from the industry expert who works on productionalizing this technology at Google.

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