Deploying Scalable Machine Learning for Data Science/ with Dan Sullivan.

Format:

Video

Author/Creator:

Sullivan, Dan, speaker.

Contributor:

linkedin.com (Firm)

Language:

English

Genre:

Instructional films.

Educational films.

Video recordings.

Physical Description:

1 online resource

polychrome

Place of Publication:

Carpenteria, CA:: linkedin.com, 2018.

System Details:

Latest version of the following browsers: Chrome, Safari, Firefox, or Internet Explorer. Adobe Flash Player Plugin. JavaScript and cookies must be enabled. A broadband Internet connection.

Summary:

Learn how to use design patterns for scalable architecture and tools such as services and containers to deploy machine learning at scale.

Machine learning models often run in complex production environments that can adapt to the ebb and flow of big data. The tools and practices that help data scientists rapidly build machine learning models are not sufficient to deploy those models at scale. To deliver scalable solutions, you need a whole new toolset. This course provides data scientists and DevOps engineers with an overview of common design patterns for scalable machine learning architectures, as well as tools for deploying and maintaining machine learning models in production. Instructor Dan Sullivan reviews three technologies that enable scalable machine learning: services that expose models through APIs, containers for deploying models, and orchestration tools like Kubernetes that help manage containers and clusters. Plus, get tips for monitoring the performance of your services in production environments.

Participant:

Presenter: Dan Sullivan

Notes:

8/17/20181

Access Restriction:

Restricted for use by site license.