Google Vertex AI

by Stephen M. Walker II, Co-Founder / CEO

What is GCP Vertex?

GCP Vertex, officially known as Google Cloud Vertex AI, is a managed machine learning (ML) platform that developers, data scientists, and researchers use to build, deploy, and manage high-quality, scalable ML models. These models are then capable of making predictions from data. The features of GCP Vertex AI cover the full spectrum of ML workflows including training, evaluation, prediction, and model versioning among others. It allows users to utilize Google's pre-trained models or create their own custom models, thereby enabling them to transform their ideas into practical applications seamlessly.

What can I build with GCP Vertex?

Google Cloud's Vertex AI is a unified platform for machine learning (ML) and artificial intelligence (AI) applications. It allows you to train, deploy, and customize ML models and AI applications, including large language models (LLMs).

Vertex AI combines data engineering, data science, and ML engineering workflows, enabling teams to collaborate using a common toolset and scale applications using the benefits of Google Cloud.

Top tip

Klu LLM Platform integrates with Google PaLM 2 models (Text & Chat Bison), which offer superior performance in certain use cases that demand quick responses and translation capabilities.

Klu Studio LLM Provider GCP Vertex

Vertex AI provides several options for model training and deployment. AutoML allows you to train models on tabular, image, text, or video data without writing code or preparing data splits. Custom training gives you complete control over the training process, including using your preferred ML framework, writing your own training code, and choosing hyperparameter tuning options.

Google Vertex AI Product Stack

Model Garden lets you discover, test, customize, and deploy Vertex AI and select open-source models and assets. Generative AI gives you access to Google's large generative AI models for multiple modalities.

Klu Google Vertex Model Garden

Vertex AI also includes a feature store, which offers a new approach to feature management by letting you maintain and serve your feature data from a BigQuery data source. This feature store acts as a metadata layer that provides online serving capabilities to your feature data source in BigQuery and lets you serve features online based on that data.

Klu Google Chat Studio

The platform is designed to support the entire machine-learning workflow, including data preparation, model training, model evaluation, model deployment, model monitoring, and model explainability. It also supports open-source frameworks like PyTorch and TensorFlow, making it an ideal target platform for any organization wanting to move their models to the cloud.

Klu Google Chat Studio

High-profile Vertex AI customers include Ford, Seagate, Wayfair, Cashapp, Cruise, and Lowe's. The pay-as-you-use resource billing model makes Vertex AI a potential choice for smaller organizations as well. However, it's important to note that Vertex AI is not free. As a cloud-based platform, Vertex AI is billed according to the compute resources and services used.

How does GCP Vertex work?

GCP Vertex is a managed machine learning platform provided by Google Cloud. It is designed to make it easier for developers to build, deploy, and scale AI models. It provides a unified UI for all AI and machine learning operations, making it ideal for applications that require fast development and deployment of complex machine learning models.

Klu Google Chat Studio

In GCP Vertex, models are trained using Google's advanced machine learning algorithms and can be deployed in the cloud or on-premises. This allows developers to focus on building their applications, without having to worry about the underlying infrastructure.

Klu Google Chat Studio

GCP Vertex is designed to handle large volumes of data and can scale horizontally to accommodate growing data needs. It also supports distributed computing, allowing it to process large amounts of data in parallel, which can significantly improve performance.

What are some common use cases for GCP Vertex?

GCP Vertex is used in a wide range of applications, particularly those that involve complex data and require fast development and deployment of machine learning models. Some common use cases include:

  1. Machine Learning and Artificial Intelligence — GCP Vertex is ideal for building and deploying the high-dimensional models used in machine learning and AI. It can handle the large volumes of data required for training models, and its advanced machine learning algorithms make it well-suited for tasks such as predictive analytics and natural language processing.

  2. Big Data Analytics — GCP Vertex can handle the large volumes of data involved in big data analytics. It can process large amounts of data in parallel, making it ideal for tasks such as real-time analytics and data mining.

  3. Recommendation Systems — GCP Vertex can be used to build recommendation systems, such as those used in e-commerce websites or streaming platforms. Its ability to process large volumes of data and its advanced machine learning algorithms make it well-suited for tasks such as predicting user preferences and recommending products or content.

  4. Image and Video Recognition — GCP Vertex can be used to build image and video recognition models, such as those used in security systems or social media platforms. Its advanced machine learning algorithms make it well-suited for tasks such as image classification and object detection.

  5. Natural Language Processing (NLP) — GCP Vertex can be used to build NLP models, such as those used in chatbots or voice assistants. Its advanced machine learning algorithms make it well-suited for tasks such as sentiment analysis and language translation.

What are some common GCP Vertex tools?

There are several tools available for working with GCP Vertex. Some of the most popular include:

  1. Vertex AI Workbench — This is an integrated development environment (IDE) for machine learning that provides a collaborative space for data scientists and developers to build, train, and deploy models.

  2. Vertex Pipelines — This tool allows developers to orchestrate machine learning workflows, which can be complex and involve multiple steps, in a scalable, repeatable, and reliable way.

  3. Vertex TensorBoard — This is a suite of visualization tools for machine learning experiments. It allows developers to track and visualize metrics such as loss and accuracy, visualize model graphs, view histograms of weights, biases, or other tensors as they change over time, and much more.

These tools provide a range of features for working with GCP Vertex, from model building and training to deployment and monitoring.

What are the leading managed machine learning platforms for AI projects?

Apart from GCP Vertex, there are other managed machine learning platforms that provide efficient and scalable solutions for building, deploying, and managing AI models. These include Amazon SageMaker, Azure Machine Learning, and IBM Watson Studio.

Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning models quickly. SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.

Azure Machine Learning is a cloud-based environment you can use to train, deploy, automate, manage, and track ML models. It supports a wide range of machine learning and deep learning frameworks.

IBM Watson Studio is a platform for businesses to prepare and analyze data, build machine learning models, and deploy and manage AI-powered applications. It provides tools for data scientists, application developers, and subject matter experts to collaboratively and easily work with data and use that data to build, train, and deploy models.

All these platforms are designed to handle large-scale datasets, support real-time analytics and queries, and offer improved performance and reduced latency in machine learning and AI applications. They also provide efficient storage and indexing of high-dimensional data and can handle vectors derived from complex data types such as images, videos, and natural language text.


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