Presentation: [CANCELED] Go for ML/AI

Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Duration: 12:50pm - 1:00pm

Day of week: Tuesday

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Abstract

Applied AI workflows should maintain a high level of integrity in production and be able to fit into the modern infrastructure that is being utilized across industry and research. As it turns out, much of this modern infrastructure is being driven by the open source Go programming language, especially as related to distributed systems, and an increasing number of individuals and companies (Dell/EMC, The New York Times, and more) are writing their mission critical ML/AI systems in Go. In this talk, I will give you a brief intro to ML/AI in Go.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Daniel Whitenack

Data Scientist, Lead Developer Advocate @pachydermIO

Daniel is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (Datapalooza, DevFest Siberia, GopherCon, and more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

Find Daniel Whitenack at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.