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

Workshop : Python-Based AI Workflows - From Notebook to Production Scale

Talk : [CANCELED] Go for ML/AI

Talk : [CANCELED] Docker-izing your Data Science Applications Codelab

Other talks from track Hands-on Codelabs & Speakers Office Hours

Parallel processing and machine learning @Google
Parallel processing and machine learning @Google
Developer Advocate @IBM
Parallel processing and machine learning @Google
Parallel processing and machine learning @Google
Spark Committer & Open Source Developer Advocate
Engineering Manager @Linkedin focused on Big Data

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.