Di google I/O 2023 ada membahas tentang Machine Learning with Google Cloud. Google I/O is an annual developer conference held by Google in Mountain View, California. The name “I/O” is taken from the number googol, with the “I” representing the “1” in googol and the “O” representing the primary “0” within the number. The format of the event is comparable to Google Developer Day. Di sana disebutkan ada 10 ways to make use of machine learning with Google Cloud. Mari kita bahas satu-satu.

In case you attempting to construct a prototype to categorise imagegs, specifically for identifying flower species base on some photos I even have. I dont have quite a lot of photos yet. But, I just want the fastest option to get a fast proof of concept out the door. Teachable machine is the reply, Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone. It allows users to coach a pc to acknowledge images, sounds, and poses without writing any machine learning code.

Teachable Machine is an excellent tool for anyone who desires to study machine learning or who desires to create their very own machine learning models. It is simple to make use of and doesn’t require any prior knowledge of machine learning.

Listed below are a number of the things you’ll be able to do with Teachable Machine:

• Create a model that may recognize images of various objects.
• Create a model that may recognize sounds of various animals.
• Create a model that may recognize poses of various people.
• Use the model in your individual projects, sites, apps, and more.

Teachable Machine is a strong tool that could be used to create a wide range of machine learning models. It is simple to make use of and accessible to everyone, making it an excellent option to study machine learning or to create your individual machine learning models.

In case you developer and have an API where you need to add some ML powered capabilities while not having to construct or deploy any models myself, specifically task Images labelling, sentiment evaluation. You should utilize ML APIs.

Here’s an inventory of Machine Learning APIs I even have experimented with:

• Vision API
• Speech to Text API
• Text to Speech API
• Natural Language API
• Video Intelligence API
• Translation API

You’ll be able to try video above to trying out ML APIs on Google Cloud

In case you attempting to construct an enterprise grade image classification model. But you dont really have the expertise, and your model must be really accurate. You should utilize AutoML in Vertex AI.

is a machine learning (ML) platform that permits you to train and deploy ML models and AI applications. Vertex AI combines data engineering, data science, and ML engineering workflows, enabling your teams to collaborate using a typical toolset.

enables developers with limited machine learning expertise to coach high-quality models specific to their business needs. Construct your individual custom machine learning model in minutes.

For more details, you’ll be able to try this video:

If you need to generate images and text, you’ll be able to start with Generative AI Studio.

Generative AI is a form of artificial intelligence (AI) that may create latest content, equivalent to text, images, and even music. Generative AI models are trained on large datasets of existing content, after which they use the data or underlying structure of the information to generate latest content that is comparable to the information they were trained on.

Generative AI Studio is a Google Cloud console tool for rapidly prototyping and testing generative AI models. You’ll be able to test sample prompts, design your individual prompts, and customize foundation models to handle tasks that meet your application’s needs.

You’ll be able to try video below for more detail.

In case you in search of a single place where I can search, discover, and use models that could be availabel to you on google cloud, You’ll be able to began with Vertex AI Model Garden.

Google Vertex Model Garden is a set of pre-built machine learning models and tools designed to simplify the means of constructing and deploying machine learning models. Model Garden provides enterprise-ready foundation models, task specific models, and APIs. Kick off a wide range of workflows including using models directly, tuning models in Generative AI Studio, or deploying models to a knowledge science notebook.

These models could be easily customised to suit specific use cases by modifying the training data or adjusting the hyperparameters. At once Vertex Model supports three classes of Model Type:

• First-party models
• Open Source models
• Third Party Models

Certainly one of the important thing advantages of Vertex Model Garden is that it allows users to quickly and simply deploy machine learning models in a production environment. That is achieved through integration with other Google Cloud Platform services, equivalent to Google Kubernetes Engine and Cloud Functions, which allows users to deploy their models as web services or serverless functions.