Vertex AI: What is it & How You Can Start Using It Today!

Vertex AI is a comprehensive suite of machine learning tools that allows developers to build, deploy, and scale Machine Learning models faster and more efficiently.

It is designed to be a fully managed service, providing a unified platform for all your AI needs. 

The main purpose of Vertex AI is to simplify the process of implementing machine learning models.

It offers a range of benefits, including the ability to accelerate machine learning workflows, reduce training time and costs, and manage models with confidence.

Key Features of Vertex AI

One AI platform, every ML tool you need

Vertex AI is designed to be a one-stop-shop for all your machine learning needs. 

It provides a wide range of tools and services, allowing you to choose the model that best fits your needs. 

Vertex AI Model Garden is a single place to search, discover, and interact with a wide variety of foundation models from Google and Google partners, available on Vertex AI. 

You can kick off a variety of workflows including using models directly, tuning models in Generative AI Studio, or deploying models to a data science notebook.

End-to-end MLOps

With Vertex AI, you can implement MLOps practices to efficiently scale, manage, monitor, and govern your ML workloads. 

This helps to reduce the work needed to maintain model performance in production, enabling data scientists and ML engineers to focus on innovation. 

Vertex AI provides purpose-built tools for data scientists and ML engineers to efficiently and responsibly automate, standardize, and manage ML projects throughout the entire development life cycle. 

Using Vertex AI you can easily train, test, monitor, deploy, and govern ML models at scale, reducing the work needed to maintain model performance in production and enabling data scientists and ML engineers to focus on innovation code.

Data and AI integration

Vertex AI is natively integrated with BigQuery, Dataproc, and Spark, allowing you to create and execute machine learning models using standard SQL queries on existing business intelligence tools and spreadsheets. 

You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. 

Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Low-code and no-code tooling

Vertex AI provides low-code and no-code tooling, making it easier for practitioners with a wide variety of expertise to leverage machine learning workloads. 

With Generative AI Studio, developers can tune and deploy foundation models for their use cases via a simple UI. 

And, with their off the shelf APIs, developers can easily call upon pre-trained models to quickly solve real-world problems.

Open and flexible AI infrastructure

The infrastructure of Vertex AI is designed to be open and flexible, making it easy to deploy ML models for any use case. 

It provides a broad selection of ML infrastructure and model deployment options, helping you to meet all your ML inference needs. 

It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden.

Benefits of Using Vertex AI

Building with generative AI

With Vertex AI, you can easily access a variety of foundation models via developer-friendly APIs on Model Garden. 

This allows you to customize, uptrain, and fine-tune models to fit your needs with Generative AI Studio.

Accelerating models to production

Vertex AI provides purpose-built tools for training, tuning, and deploying ML models. This helps to reduce training time and cost with optimized AI infrastructure.

Managing models with confidence

Vertex AI simplifies the complexity of model maintenance with MLOps tooling such as Vertex AI Pipelines, to streamline running ML pipelines, and Vertex AI Feature Store to serve, and use AI technologies as ML features. This allows you to manage your models with confidence, streamlining the running of ML pipelines and serving AI technologies as ML features.

Customer Success Stories

There are numerous case studies of customers who have benefited from using Vertex AI. 

These stories highlight the practical applications and successes of using this platform in various industries and use cases. 

For instance, companies like Wayfair and Vodafone have been able to accelerate their MLOps and power great experiences at scale using Vertex AI.

Getting Started with Vertex AI

If you’re interested in using Vertex AI, there are plenty of resources and documentation available to help you get started. 

This includes tutorials and guides that provide step-by-step instructions on how to use the platform’s features and tools. 

For instance, you can learn how to use Vertex AI Workbench to train a TensorFlow model with data from BigQuery, or explore recommendations for using Vertex AI for common use cases.

Use Cases of Vertex AI

Prototype to Production

Vertex AI helps you go from notebook code to a deployed model in the cloud. 

From data to training, batch or online predictions, tuning, scaling and experiment tracking, Vertex AI has every tool you need. 

You can get started with Notebooks, a managed notebook environment for data exploration, experimentation, and model training. 

Then, you can learn how to package up your training code with Docker and run a custom container training job on Vertex AI. 

Finally, when you’re ready to use your model to solve a real-world problem with ML, you can use the Vertex AI prediction service for batch and online predictions.

Data readiness

Vertex AI supports your data preparation process. 

You can ingest data from BigQuery and Cloud Storage and leverage Vertex AI Data Labeling to annotate high-quality training data and improve prediction accuracy.

Feature engineering

Use Vertex AI Feature Store, a fully managed rich feature repository, to serve, share, and reuse ML features. 

Vertex AI Experiments can be used to track, analyze, and discover ML experiments for faster model selection. 

Vertex AI TensorBoard can be used to visualize ML experiments, and Vertex AI Pipelines can be used to simplify the MLOps process by streamlining the building and running of ML pipelines.

Training and hyperparameter tuning

Build state-of-the-art ML models without code by using AutoML to determine the optimal model architecture for your image, tabular, text, or video-prediction task, or build custom models using Notebooks. 

Vertex AI Training offers fully managed training services, and Vertex AI Vizier provides optimized hyperparameters for maximum predictive accuracy.

Model serving

Vertex AI Prediction makes it easy to deploy models into production, for online serving via HTTP or batch prediction for bulk scoring. 

You can deploy custom models built on any framework (including TensorFlow, PyTorch, scikit or XGB) to Vertex AI Prediction, with built-in tooling to track your models’ performance.

Model tuning and understanding

Get detailed model evaluation metrics and feature attributions, powered by Vertex Explainable AI. 

Vertex Explainable AI tells you how important each input feature is to your prediction. Available out of the box in AutoML Forecasting, Vertex AI Prediction, and Vertex AI Workbench.

Model monitoring

Continuous monitoring offers easy and proactive monitoring of model performance over time for models deployed in the Vertex AI Prediction service. 

Continuous monitoring monitors signals for your model’s predictive performance and alerts when the signals deviate, diagnose the cause of the deviation, and trigger model-retraining pipelines or collect relevant training data.

Model management

Vertex ML Metadata enables easier auditability and governance by automatically tracking inputs and outputs to all components in Vertex Pipelines for artifact, lineage,

and execution tracking for your ML workflow. You can track custom metadata directly from your code and query metadata using a Python SDK.

All Features of Vertex AI

Vertex AI offers a detailed exploration of all the features provided by the platform. 

These features include: 

• Vertex AI Model Garden
• Vertex AI Generative AI Studio
• AutoML
• Deep Learning VM Images
• Vertex AI Workbench
• Vertex AI Matching Engine
• Vertex AI Data Labeling
• Vertex AI Deep Learning Containers
• Vertex Explainable AI
• Vertex AI Feature Store 
• Vertex ML Metadata
• Vertex AI Model Monitoring 
• Vertex AI Neural Architecture Search
• Vertex AI Pipelines
• Vertex AI Prediction 
• Vertex AI Tensorboard 
• Vertex AI Training 
• Vertex AI Vizier

Each of these features has its own unique capabilities and benefits, providing a comprehensive suite of tools for your machine learning needs.

Pricing

The pricing model for Vertex AI is based on the resources used for model training, predictions, and Google Cloud product resource usage. 

You can get full pricing rates or estimate your costs with the google cloud pricing calculator. 

This allows you to plan your budget and ensure that you are getting the most value from the platform.

In conclusion, Vertex AI is a powerful tool for anyone looking to leverage machine learning in their projects. 

With its wide range of features, ease of use, and flexible pricing, it is an excellent choice for both beginners and experienced professionals in the field of AI. 

Whether you are looking to build, deploy, or scale ML models, Vertex AI has the tools and resources you need to succeed.