Gwendolyn D. Stripling, PhD

Gwendolyn D. Stripling, PhD

Lead AI & ML Content Developer at Google Cloud

    Gwendolyn Stripling, Ph.D., is an Artificial Intelligence and Machine Learning Content Developer at Google Cloud. Stripling is author of the widely popular YouTube video, “Introduction to Generative AI” and of the O’Reilly Media book “Low-Code AI: A Practical Project Driven Approach to Machine Learning”. They are also the author of the LinkedIn Learning video “Introduction to Neural Networks”. Stripling is an Adjunct Professor and member of Golden Gate University's Masters in Business Analytics Advisory Board. Stripling enjoys speaking on AI/ML, having presented at Dominican University of California's Barowsky School of Business Analytics, Golden Gate University’s Ageno School of Business Analytics, and numerous Tech conferences.

    All Sessions by Gwendolyn D. Stripling, PhD

    Day 1 04/23/2024
    2:20 pm - 3:20 pm

    No-Code and Low-Code AI: A Practical Project Driven Approach to ML

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span>

    Tutorial: No-code machine learning (ML) is a way to build and deploy ML models without having to write any code. Low-code ML is a way to build and deploy ML models with minimal coding. Both methods can be valuable for businesses and individuals who do not have the skills or resources to develop ML models themselves. By completing this workshop, you will develop an understanding of no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and for building, training, and deploying ML models. You will become familiar with Google’s Vertex AI for both no-code and low-code ML model training, and Google’s Colab, a free Jupyter Notebook service for running Python and the Keras Sequential API, a simple and easy-to-use API that is well-suited for beginners. You will also become familiar with how to assess when to use low-code, no-code, and custom ML training frameworks. The primary audience for this workshop are aspiring citizen data scientists, business analysts, data analysts, students, and data scientists who seek to learn how to very quickly experiment, build, train, and deploy ML models.

    Day 1 04/23/2024
    2:20 pm - 3:20 pm

    No-Code and Low-Code AI: A Practical Project Driven Approach to ML

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> </span>

    Tutorial: No-code machine learning (ML) is a way to build and deploy ML models without having to write any code. Low-code ML is a way to build and deploy ML models with minimal coding. Both methods can be valuable for businesses and individuals who do not have the skills or resources to develop ML models themselves. By completing this workshop, you will develop an understanding of no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and for building, training, and deploying ML models. You will become familiar with Google’s Vertex AI for both no-code and low-code ML model training, and Google’s Colab, a free Jupyter Notebook service for running Python and the Keras Sequential API, a simple and easy-to-use API that is well-suited for beginners. You will also become familiar with how to assess when to use low-code, no-code, and custom ML training frameworks. The primary audience for this workshop are aspiring citizen data scientists, business analysts, data analysts, students, and data scientists who seek to learn how to very quickly experiment, build, train, and deploy ML models.

    Open Data Science

     

     

     

    Open Data Science
    One Broadway
    Cambridge, MA 02142
    info@odsc.com

    Privacy Settings
    We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
    Youtube
    Consent to display content from - Youtube
    Vimeo
    Consent to display content from - Vimeo
    Google Maps
    Consent to display content from - Google