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Executive Guide to Deploying, Monitoring, and Maintaining Models

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Keith McCormick

55:54

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  • 01 - Data and supervised machine learning.mp4
    02:09
  • 02 - Data engineering and MLOps in the ML lifecycle.mp4
    04:05
  • 03 - Why ML projects fail to be deployed.mp4
    03:59
  • 04 - The basics of ML modeling.mp4
    03:24
  • 01 - The business evaluation phase.mp4
    03:32
  • 02 - A deployment checklist.mp4
    04:46
  • 01 - Scoring traditional ML models.mp4
    03:34
  • 02 - Scoring a black box model.mp4
    03:34
  • 03 - Scoring an ensemble.mp4
    02:56
  • 01 - Batch vs. real-time scoring.mp4
    06:02
  • 02 - Data prep and scoring.mp4
    04:20
  • 03 - Combining batch and real-time scoring.mp4
    02:10
  • 01 - What is model monitoring.mp4
    05:49
  • 02 - How often should you rebuild.mp4
    05:34
  • Description


    With recent developments in the AI space, workflows for deploying, monitoring, and maintaining ML models have changed. In this course, Keith McCormick—an independent data miner, trainer, speaker, and author—breaks down the phases of an ML project and guides you through model evaluation, scoring, deployment, and model maintenance. Learn about data engineering and MLOps in the ML lifecycle, as well as the basics of ML modeling. Get a useful deployment checklist that you can use in model evaluation. Find out how to score traditional ML models, a “black box” model, and an ensemble. Go over batch and real-time scoring. Plus, explore model monitoring and the best frequency for model rebuilding.

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    Keith McCormick
    Keith McCormick
    Instructor's Courses
    I'm an independent consultant, trainer, speaker, and author of seven books. My consulting specializes in helping analytics leaders build and manage their data science teams. My training, including 20 LinkedIn Learning courses and frequent conference workshops, has reached 1000s of individuals trying to learn statistics, machine learning, and data science. I love that I am able to train and consult. Training allows me to interact with (and learn from) 100s of clients in dozens of industries every year. It prevents me from obtaining too narrow a focus, and it keeps me current. Consulting allows me to work with a smaller number of clients in detail and in-depth, working with them on real problems of immediate concern to them. It keeps me sharp. If you've encountered me through my LinkedIn Learning courses, please consider following me here on LinkedIn. I'm not able to connect with everyone, so I connect only with clients and colleagues that I know directly. But please do follow me here because I'm quite active on LinkedIn and frequently post excerpts from the courses and other content. Follow #freefirstfridays to see when I post a link to watch a course for free. My favorite kind of consulting work involves: - working with analytics management to create effective data science teams - listening carefully to my client explain their business in detail - turning their description into a research question that can be answered with their data - coaching my client on presenting possible solutions to decision-makers - working behind the scenes to get the solution deployed Specialties: For the last several years, my emphasis has been working with analytics management to more efficiently run their teams and to nurture new hires as they expand their teams. I am skilled at explaining complex methods to new users or decision-makers and can do so at any level of technical detail. I specialize in predictive models and segmentation analysis, including classification trees, neural nets, general linear model, cluster analysis, and association rules. Books and Courses The best way to find out more about me is to check out my courses on LinkedIn Learning. They have received over 500,000 views, and each one has some free content. My books can be found on Amazon, and typically that allows you to view some free content as well. I'm very proud of all of this content (listed below in my profile), but I am still primarily an active consultant. If you need consulting help, private training, or a keynote speaker, contact me, and we can discuss.
    LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
    • language english
    • Training sessions 14
    • duration 55:54
    • English subtitles has
    • Release Date 2024/06/22