AutoML

1. AutoML

Another more custom way to use machine learning to solve problems is to train models by using your own data. This is where Vertex AI comes in. Vertex AI brings together Google Cloud services for building ML under one unified user interface. You can use your own training data with Vertex AI to manage and build ML projects. AutoML and Vertex AI lets you build and train machine learning models from end to end by using graphical user interfaces. Often referred to as GUIs without writing a line of code. This means that after your data is ingested into Vertex AI, AutoML chooses the best machine learning model for you by comparing different models and tuning parameters. What once required, a lot of manual work is done automatically and quickly, which results in a trained model that is both accurate and customized to your data. This lets machine learning practitioners focus on the problems that they are trying to solve. Instead of the details of machine learning. AutoML is a great option for businesses that want to produce a customized ML model, but are not willing to spend too much time coding and experimenting with thousands of models. Let's go back to our image recognition example, which used Vision API, a pre-existing model trained with Google data. Imagine you work for a car manufacturing company. Vision API can tell you the difference between generic images found in Google databases, like the difference between a wheel and a door. But it can't help a car manufacturer distinguish between good or defective parts. In this case, a developer could use an AutoML vision instance and train it with your specialized data. This automates the training of machine learning models, which means that you could upload a batch of images and train an image classification model through the easy to use graphical interface of AutoML. Models can be further optimized and deployed directly from the cloud. Now let's focus on another feature of AutoML. Earlier you saw how the natural language API could be used for processing entries into an online contact form. But if your text examples don't fit neatly into the natural language API, sentiment based or vertical topic based classification scheme, and you want to use your own specialized data instead, you need to use AutoML natural language. AutoML natural language lets you build and deploy custom machine learning models. The analyzed documents, categorize them, identify entities within them, or assess attitudes within them. You can use the AutoML user interface to upload your training data and test your custom model without a single line of code. Vertex AI makes this customization possible. Those examples are just a few of the many Google Cloud ML offerings. You can also find APIs that categorize videos, convert audio to text, or text to audio, understand natural language, translate from one language to another, and more. In fact, in many of the most innovative applications for machine learning, several of these applications are combined.

2. Let's practice!

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