Google Cloud Digital Leader: AI & ML on Google Cloud

Google Cloud 2024    |    Beginner
  • 14 videos | 1h 54m 13s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 2 users Rating 4.5 of 2 users (2)
Leveraging artificial intelligence (AI) and machine learning (ML) is essential for powering innovation and data-driven decision-making. In this course, you will explore the foundational concepts of AI and ML, including features, models, and training, and you will contrast AI/ML with business intelligence. Next, you will discover generative AI's capabilities and the attributes of good data, emphasizing quality, diversity, and relevance for effective machine learning. Additionally, you will delve into responsible AI, learning ethical considerations and best practices for developing fair, transparent, and accountable AI systems. Finally, you will examine Google Cloud's Vertex AI platform, perform machine learning on Google Cloud using tools like AutoML for automated training and custom training for tailored solutions, and take a look at TensorFlow, BigQuery ML, and pre-trained application programming interfaces (APIs) for common use cases such as vision, video, and speech. This course helps prepare learners for the Google Cloud Digital Leader certification exam.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Compare and contrast ai and ml
    Define common terms in the ml workflow
    Contrast ai and ml with business intelligence
    Define generative ai and differentiate between generative and discriminative models
    Enumerate the attributes of good data
    Cite tenets of responsible ai
  • Provide an overview of the place of vertex ai among google cloud offerings
    Analyze bigquery ml, pre-trained application programming interfaces (apis), automl and custom trained models
    Contrast automl and custom training
    Outline the features of tensorflow and the roles of cpus, graphics processing units (gpus), and tensor processing units (tpus)
    Evaluate different ml training options on the google cloud using multiple criteria
    Draw the workflow of generative ai use cases on google cloud
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 43s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 11m 43s
    After completing this video, you will be able to compare and contrast AI and ML. FREE ACCESS
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    3.  Common Terms in Machine Learning
    7m 15s
    Upon completion of this video, you will be able to define common terms in the ML workflow. FREE ACCESS
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    4.  AI/ML vs. Business Intelligence
    8m 34s
    After completing this video, you will be able to contrast AI and ML with business intelligence. FREE ACCESS
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    5.  Generative AI
    8m 3s
    Upon completion of this video, you will be able to define generative AI and differentiate between generative and discriminative models. FREE ACCESS
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    6.  Attributes of Good Data
    13m 15s
    After completing this video, you will be able to enumerate the attributes of good data. FREE ACCESS
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    7.  Responsible AI
    10m 45s
    Upon completion of this video, you will be able to cite tenets of responsible AI. FREE ACCESS
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    8.  Vertex AI
    11m 59s
    After completing this video, you will be able to provide an overview of the place of Vertex AI among Google Cloud offerings. FREE ACCESS
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    9.  Machine Learning on Google Cloud
    12m 6s
    Upon completion of this video, you will be able to analyze BigQuery ML, Pre-trained application programming interfaces (APIs), AutoML and custom trained models. FREE ACCESS
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    10.  AutoML and Custom Training
    11m 42s
    After completing this video, you will be able to contrast AutoML and custom training. FREE ACCESS
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    11.  TensorFlow and Compute Infrastructure
    5m 41s
    Upon completion of this video, you will be able to outline the features of TensorFlow and the roles of CPUs, graphics processing units (GPUs), and tensor processing units (TPUs). FREE ACCESS
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    12.  Choose the Right Training Solution
    5m 20s
    After completing this video, you will be able to evaluate different ML training options on the Google Cloud using multiple criteria. FREE ACCESS
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    13.  Generative AI on Vertex AI
    4m 15s
    Upon completion of this video, you will be able to draw the workflow of generative AI use cases on Google Cloud. FREE ACCESS
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    14.  Course Summary
    1m 53s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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