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Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps and aplies them to creating and delivering production-grade data science projects in Python and R.
This book's first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organization's security, networking, and administration teams. Key Features: - Start-to-finish labs take readers through creating projects that meet DevOps best practices and creating a server-based environment to work on and deploy them.Alex leads the Solutions Engineering team at Posit (formerly RStudio). In that role, he has advised hundreds of organizations of all sizes and levels of sophistication to create production-grade open-source data science environments. Before coming to Posit, he was a data scientist and data science team lead and worked on politics, consulting, and healthcare.
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