Introduction
1. Introduction
Historically, artificial intelligence and machine learning were not accessible to ordinary people. Most of the people capable of developing AI and ML solutions were specialty engineers, who were scarce in number and expensive to hire. The reality is that ML is more accessible now than ever before, which allows more people to build, even those without the technical expertise. Google Cloud offers four options for building machine learning models. The first option is BigQuery ML. This is a tool for using SQL queries to create and execute machine learning models in BigQuery. If you already have your data in BigQuery and your problems fit the predefined ML models, this could be your choice. The second option is to use pre trained APIs, or application programming interfaces. This option lets you use machine learning models that were built and trained by Google, so you don't have to build your own ML models if you don't have enough training data or sufficient machine learning expertise in house. The third option is AutoML, which is a no code solution, letting you build your own machine learning models on Vertex AI through a point and click interface. And finally, there's custom training through which you can code your very own machine learning environment, the training, and the deployment, which gives you flexibility and provides control over the ML pipeline. In this second section of the course, you'll learn more about these four options for building machine learning models, and you'll also learn about some of Google's other AI solutions.2. Let's practice!
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