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  • 1
    Online Resource
    Online Resource
    Training and exporting machine learning models in Spark : a hands-on guide to train, score, evaluate, and export machine learning models (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (34 min., 53 sec.)) , digital, sound, color
    Keywords: SPARK (Electronic resource) ; Machine learning ; Electronic videos ; local
    Abstract: "Spark ML provides a rich set of tools and models for training, scoring, evaluating, and exporting machine learning models. This video walks you through each step in the process. You'll explore the basics of Spark's DataFrames, Transformer, Estimator, Pipeline, and Parameter, and how to utilize the Spark API to create model uniformity and comparability. You'll learn how to create meaningful models and labels from a raw dataset; train and score a variety of models; target price predictions; compare results using MAE, MSE, and other scores; and employ the SparkML evaluator to automate the parameter-tuning process using cross validation. To complete the lesson, you'll learn to export and serialize a Spark trained model as PMML (an industry standard for model serialization), so you can deploy in applications outside the Spark cluster environment."--Resource description page.
    Note: Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Monitoring and improving the performance of machine learning models : how to use ModelDB and Spark to track and improve model performance over time (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (35 min., 52 sec.)) , digital, sound, color
    Keywords: SPARK (Electronic resource) ; ModelDB (Electronic resource) ; Machine learning ; Electronic videos ; local
    Abstract: "It's critical to have 'humans in the loop' when automating the deployment of machine learning (ML) models. Why? Because models often perform worse over time. This course covers the human directed safeguards that prevent poorly performing models from deploying into production and the techniques for evaluating models over time. We'll use ModelDB to capture the appropriate metrics that help you identify poorly performing models. We'll review the many factors that affect model performance (i.e., changing users and user preferences, stale data, etc.) and the variables that lose predictive power. We'll explain how to utilize classification and prediction scoring methods such as precision recall, ROC, and jaccard similarity. We'll also show you how ModelDB allows you to track provenance and metrics for model performance and health; how to integrate ModelDB with SparkML; and how to use the ModelDB APIs to store information when training models in Spark ML. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; cloud platforms like Amazon Web Services; Bash, Docker, and REST."--Resource description page.
    Note: Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Deploying machine learning models as microservices using Docker : a REST-based architecture for serving ML model outputs at scale (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (24 min., 30 sec.)) , digital, sound, color
    Keywords: Machine learning ; Application software ; Development ; Electronic videos ; local
    Abstract: "Modern applications running in the cloud often rely on REST-based microservices architectures by using Docker containers. Docker enables your applications to communicate between one another and to compose and scale various components. Data scientists use these techniques to efficiently scale their machine learning models to production applications. This video teaches you how to deploy machine learning models behind a REST API, to serve low latency requests from applications, without using a Spark cluster. In the process, you'll learn how to export models trained in SparkML; how to work with Docker, a convenient way to build, deploy, and ship application code for microservices; and how a model scoring service should support single on-demand predictions and bulk predictions. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; cloud platforms like Amazon Web Services; Bash, Docker, and REST."--Resource description page.
    Note: Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Deploying Spark ML pipelines in production on AWS : how to publish pipeline artifacts and run pipelines in production (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (23 min., 20 sec.)) , digital, sound, color
    Keywords: Amazon Web Services (Firm) ; SPARK (Electronic resource) ; Machine learning ; Cloud computing ; Electronic books ; Electronic videos ; local
    Abstract: "Translating a Spark application from running in a local environment to running on a production cluster in the cloud requires several critical steps, including publishing artifacts, installing dependencies, and defining the steps in a pipeline. This video is a hands-on guide through the process of deploying your Spark ML pipelines in production. You'll learn how to create a pipeline that supports model reproducibility--making your machine learning models more reliable--and how to update your pipeline incrementally as the underlying data change. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Amazon Web Services such as S3, EMR, and EC2; Bash, Docker, and REST."--Resource description page.
    Note: Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines (2017)
    [Place of publication not identified] : O'Reilly
    Details
    Language: English
    Pages: 1 online resource (1 streaming video file (39 min., 56 sec.)) , digital, sound, color
    Keywords: Machine learning ; Cloud computing ; Quantitative research ; Mathematical statistics ; Data processing ; Electronic books ; Electronic videos ; local
    Abstract: "This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline. You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production. The course is designed for the data engineer transitioning to the cloud and for the data scientist ready to use model deployment pipelines that are reproducible and automated. Learners should have basic familiarity with: cloud platforms like Amazon Web Services; Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Bash, Docker, and REST."--Resource description page.
    Note: Title from title screen (Safari, viewed January 15, 2018). - Release date from resource description page (Safari, viewed January 15, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
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