Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Meet the Expert : Mark Treveil on MLOps (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 59 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Machine learning operations, or MLOps, is a set of processes that can help today’s organizations get value from data science by reducing friction throughout pipelines and workflows. However, implementing MLOps is easier said than done because it touches so many teams, people, and processes across the organization — it’s larger than just model monitoring in production. Through his experience working with global organizations on governance and MLOps topics, Mark will outline the key components of a robust (and successful) MLOps strategy. O'Reilly Meet the Expert explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions. This event is for you because… You want to design MLOps systems that minimize risks across the organization. You want to get more value out of data science by implementing MLOps processes. Prerequisites: Come with your questions for Mark Treveil Have a pen and paper handy to capture notes, insights, and inspiration
    Note: Online resource; Title from title screen (viewed April 8, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    MLOps – Kernkonzepte im Überblick (2021)
    [Erscheinungsort nicht ermittelbar] : dpunkt | Boston, MA : Safari
    Details
    Language: English , German
    Pages: 1 online resource (204 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Viele Machine-Learning-Modelle, die in Unternehmen entwickelt werden, schaffen es aufgrund von organisatorischen und technischen Hürden nicht in den produktiven Betrieb. Dieses Buch zeigt Ihnen, wie Sie erprobte MLOps-Strategien einsetzen, um eine erfolgreiche DevOps-Umgebung für Ihre ML-Modelle aufzubauen, sie kontinuierlich zu verbessern und langfristig zu warten. Das Buch erläutert MLOps-Schlüsselkonzepte, mit denen Data Scientists und Data Engineers ihre ML-Pipelines und -Workflows optimieren können. Anhand von Fallbeispielen, die auf zahlreichen MLOps-Anwendungen auf der ganzen Welt basieren, geben neun ML-Experten wertvolle Einblicke in die fünf Schritte des Modelllebenszyklus - Build, Preproduction, Deployment,Monitoring und Governance. Sie erfahren auf diese Weise, wie robuste MLOps-Prozesse umfassend in den ML-Produktlworkflow integriert werden können.
    Note: Online resource; Title from title page (viewed August 1, 2021) , Mode of access: World Wide Web.
    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
    What Is MLOps? (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (54 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: For years, organizations have struggled to move data science, machine learning, and AI projects from the realm of experimental to having real business impact. One reason is because pivoting operations around these technologies involves more than just technology--the orchestration of people and processes is also critically important. In the wake of the global health crisis, the need for structure around building and maintaining machine learning models (much less tens, hundreds, or thousands of them) has only grown. With this report, business leaders will learn about MLOps, a process for generating long-term value while reducing the risk associated with data science, ML, and AI projects. Authors Lynn Heidmann and Mark Treveil from Dataiku start by introducing the data science-ML-AI project lifecycle to help you understand what--and who--drives these projects. You'll explore: Detailed components of ML model building, including how business insights can provide value to the technical team Monitoring and iteration steps in the AI project lifecycle--and the role business plays in both processes How components of a modern AI governance strategy are intertwined with MLOps Guidelines for aligning people, defining processes, and assembling the technology necessary to get started with MLOps
    Note: Online resource; Title from title page (viewed November 25, 2020) , Mode of access: World Wide Web.
    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
    Introducing MLOps (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (26 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach-Build, Manage, Deploy and Integrate, and Monitor-for creating ML-infused applications within your organization. You'll learn how to: Fulfill data science value by reducing friction throughout ML pipelines and workflows Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action
    Note: Online resource; Title from title page (viewed February 25, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...