Vertex AI: Transforming Machine Learning Possibilities

The rapid development of the artificial intelligence technology market contributes to the emergence of powerful multifunctional systems for AI/ML developers, engineers, and data analysts. In this article, we will tell you about one of these platforms created by Google. You will learn what Google Vertex AI is, what tools, capabilities, and advantages it offers, as well as what purposes and areas it is used for.

Vertex AI is a platform with a set of tools for developing and applying generative artificial intelligence (AI) and machine learning (ML) technologies. It is part of Google Cloud Services (GCS) and includes over 160 generative AI models, including both proprietary (Gemini, Imagen, Veo, etc.) and open-source models (Gemma, Llama 3.1, Mistral, etc.). In addition, it contains a number of useful tools for developers, ML engineers, and data scientists (Vertex AI Studio, Agent Builder, and more).

The platform helps simplify and speed up the processes of developing, testing, deploying, and managing AI/ML models. Its users can implement ready-made models in their projects, as well as train their own on pre-prepared data sets. The system supports all stages of the machine learning process: data preparation, training, evaluation, and deployment of the model, as well as its fine-tuning and monitoring.

Vertex functionality is directly integrated with other Google Cloud products, which significantly expands its capabilities. For example, you can use data from cloud storage to train models or the BigQuery system to analyze big data. Other useful integrations include a number of popular ML frameworks, including PyTorch and TensorFlow.

Vertex AI pricing offers separate pricing for different tools, which is determined by the amount of computing resources and cloud storage used. The service charges for every 1,000 characters of input (prompt) and 1,000 characters of output (answer). The number of characters is calculated by UTF-8 code points, excluding spaces.

Prediction queries are priced based on the input data volume only. Generative AI models start at $0.0001 per 1,000 characters of input and 1,000 characters of output. For AutoML models, it starts at $1.375 per node hour for images and $0.462 per node hour for videos.

The prices for training custom models using the platform resources depend on the equipment used, the region, the availability and type of accelerators. Users can calculate the approximate price using the calculator on the website or contact the sales department.

It is recommended to check the current pricing on the official Google Cloud website.

Vertex AI has a powerful set of tools that you can use to create, optimize, and deploy machine learning models. This platform is suitable for complex work on AI/ML projects, as it provides not only generative AI but also the ability to automate training and manage ready-made models. We invite you to familiarize yourself with the main tools and functions of Vertex AI, which have made it a universal system for developing ML technologies and their implementation in various processes and applications.

The core of Vertex functionality is a serious set of technologies designed for machine learning automation. Available large language models (LLM) can generate different types of content: text, images, program code, as well as speech and video. The platform allows not only the use of already trained models but also the creation of your own models from scratch and customization at your discretion. Using generative AI algorithms and a number of tools, engineers can develop prototypes, customize, integrate, and deploy them in applications automatically.

Support for GPT and BERT allows specialists to effectively work with advanced natural language processing systems to create new chatbots, improve text analysis, and facilitate cross-language translations. In addition, generative AI on the platform can be used to integrate solutions in the process of creating visual content. This will be very useful in the creative industry and in the development of multimedia applications.

Machine learning operations (MLOps) performed by Google Vertex AI automate and optimize the deployment and monitoring of AI/ML models. One of the main such operations is AutoML. It involves the automatic training of models without writing code or manually preparing data. Using MLOps improves the correctness and efficiency of model lifecycle management at all stages: from training to monitoring its operation and maintenance.

MLOps tools support multiple clouds and can integrate with different cloud platforms. This allows you to scale your machine learning operations. They also allow you to monitor the performance of your model. This is essential for quickly identifying and fixing issues in real time.

The platform comes with a comprehensive set of tools for managing machine learning models and the data they process. Among them are ready-made Vertex AI Datasets for data preparation and Vertex AI Model for model management. They speed up and simplify model training, evaluation, and deployment, as well as creating the endpoints needed for forecasting in batch or real-time.

The platform has tools that automatically clean data and pre-process it for further processing. They help to quickly and efficiently improve the quality of incoming data for the model. In addition, integrations with various data storage systems, such as BigQuery, allow you to successfully manage large volumes of information.

The Model Garden console library in Google's Vertex AI helps developers discover, tune, test, and deploy Google-built machine learning models. It gives them access to a variety of open-source models and the resources they need to get started.

Model Garden integrates with other useful tools and frameworks, such as TensorFlow, PyTorch, and JAX. These greatly expand the capabilities the library provides for working with machine learning models. Engineers can use it to conduct experiments, tune model parameters, and evaluate its performance when testing in real applications. Moreover, Model Garden’s ready-made solutions and optimization of computing resources help speed up the development process.

Vertex AI supports the creation of custom models from scratch and their further customization according to unique business requirements. This is facilitated by native platform integrations with popular machine learning toolkits and frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, Keras, and others. In addition, Vertex can be easily integrated with Slack, Google Sheets, Typeform, Calendly, YouTube, and other well-known online services using API or online connectors.

An impressive set of customization tools and wide integration capabilities of the platform allow developers to successfully adapt the solutions they create to different industries. Acceleration of the finished product's market launch is another significant advantage that is highly valued by businesses.

Vertex's powerful AI capabilities give the platform a number of significant advantages that are attractive to both individual developers and ML companies. The combination of extensive customization and integration capabilities with the flexibility of scaling allows you to adapt the solutions created with its help to almost any task.

Key benefits of Vertex AI:

In order to use the capabilities of Vertex AI effectively, you need to work with the platform consistently, moving from one stage to another and not skipping anything. Every step is important. The success of integrating ML into the project or application you are working on largely depends on the clarity and consistency of the engineer's actions.

Here's what the process of using Vertex AI looks like step by step:

Understanding how to use Vertex AI is important to make the most of ML capabilities in practice. Knowing how to work with this platform will allow a specialist to optimize the development and integration of created models, as well as speed up the launch of an innovative solution.

Vertex AI opens up wide opportunities for IT businesses. Companies can create custom ML models on this platform or use ready-made ones. It helps successfully integrate and quickly scale solutions in various areas: processing large data, recognizing visual content, developing NLP-based programs, and more.

Here are some examples of how these opportunities are realized in practice:

Google Cloud Vertex AI has a powerful set of tools for creating AI models. With their help, you can develop, implement, and manage AI and ML solutions more easily and quickly. The platform provides extensive opportunities for working on such progressive models as LLM, allowing you to automate and customize a number of important processes. 

The main advantages of Vertex AI are the ease of use of the platform, rapid scalability, and integration with Google Cloud services. All this allows you to develop high-level solutions in response to growing business demands. Quite affordable prices and a variety of tools make this system universal. It is equally well-suited for startups and large companies operating in the field of AI and ML.

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