Understand the Practice of Data Science in the Real World
Sponsored Track By
As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices increase. The challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI are a few of the issues organizations wrestle with.
Learn best practices for effective data science management
Sessions in this broad focus area will look at uses cases, best practices, and stories from the field to show how to effectively incorporate data science practice into the wider business process. This focus area will look beyond data sourcing and modeling towards the many challenges teams need to overcome to effectively apply data science in their organization.
Sessions on Management, Practice & DataOps Track
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Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability
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Talk: Adversarial Attacks on Deep Neural Networks
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Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow
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Workshop: Machine Learning for Digital Identity
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Talk: Adding Context and Cognition to Modern NLP Techniques
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Training: Good, Fast, Cheap: How to do Data Science with Missing Data
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Workshop: Open Data Hub workshop on OpenShift
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Talk: Practical AI solutions within healthcare and biotechnology
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Training: Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale
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Workshop: Reproducible Data Science Using Orbyter
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Talk: Combining millions of products into one marketplace using computer vision and natural language processing
Why Attend?
Some of Our Current Speakers
Who Should Attend
Data Science is cross industry and cross enterprise, impacting many different departments across job roles and functions. This track is not only for data scientists of all levels but for anyone interested in the practice and management of data science, including:
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Data scientists moving beyond model experimentation looking to understand production workflow
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Data scientists seeking to improve the overall practice of management and development
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Anyone interested in understanding better collaborative and agile management techniques as applied to data science
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Business professionals and industry experts looking to understand data science in practice
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Software engineers and technologists who need to work with data science workflows and understand the unique requirements of these systems
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CTO, CDS, and other managerial roles that require a bigger picture view of data science
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Technologists in the field of DevOps, databases, project management and others looking to break into data science
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Students and academics looking for more practical applied training in data science tools and techniques