Vertex AI is a cloud-based machine learning platform developed by Google Cloud that provides an end-to-end workflow for building, training, and deploying machine learning models. It supports various types of machine learning tasks, offers tools for data preprocessing and analysis, and includes pre-trained models for common use cases. Vertex AI eliminates the need for infrastructure management, allowing developers, data scientists, and researchers to focus on their machine learning tasks. With Vertex AI, users can train and deploy models on Google Cloud infrastructure, including AI Platform, Kubernetes, and AutoML.
Taking machine learning (ML) applications from being merely an idea to becoming a fully-fledged product used to be the domain of data scientists. Google Cloud’s Vertex AI aims to make the entire process accessible without the need to code.
The goal is to open up ML programming to a wider range of users aside from machine learning engineers. Plus, improved workflow and an easy-to-access user interface should accelerate the work of data scientists, too. In this article, we take a closer look.
The aim of Vertex AI is to bring together machine learning tools on one platform. Rather than having to search for and access different Google Cloud platforms on various machine learning platforms at different stages of the workflow, developers find everything in one place.
Ingesting data, training machine learning models, and deploying the most successful models in user-friendly apps can all be achieved from one single platform. Plus, Vertex AI’s single interface makes it easy for novice developers and those without extensive coding knowledge to develop the apps their users need.
As new developers build their expertise through Vertex pipelines, seasoned developers can reap the benefits of working in a flexible and seamless environment. In both cases, app development becomes a faster, smoother process.
A Unified Machine Learning Workflow
To understand the benefits of Vertex AI better, it is worth taking a closer look at a typical ML workflow.
Once developers have chosen a task for which they are looking to create a machine learning-based app, they need to ingest, analyze, and transform raw data. Next is creating and training a specific model to perform the task in question. Training models can often be the most time-consuming part of the process.
After that follows the evaluation stage. Here, the model may be proven to be reliable, but it could also show problems. If developers are not happy with their original model, they may choose to use another one and repeat these two stages.
Once the team has found a reliable model, they can deploy their app and run prediction tasks.
Vertex AI simplifies the entire data science workflow right from the beginning. Managed datasets support the initial data preparation stages. Data can also be labeled and annotated right on the platform. There is simply no need to switch between different services.
At the Vertex AI training stage, Auto ML is available to take care of image files, videos, text-based data, and tables. If your data is stored in these formats, there is no need to create a custom model. Vertex AI will simply select the most suitable model for prediction.
At the same time, developers do not need to compromise on insights. Vertex ML metadata allows them to record parameters and observations of an experiment. Vertex ML metadata also helps track model artifacts. Feature attributions allow users to look at Vertex AI predictions in even more detail. Feature attributions help you see which features or training code contributed most to the outcome, forming the basis of further feature engineering.
For other applications, or if developers want more control, they can use custom-trained models from frameworks and their optimal model architectures instead of pre-trained models. To facilitate this, Vertex provides Docker container images as part of its training service.
Vertex explainable AI lets you understand the reasoning behind your model’s predictions at the evaluation stage. Vertex AI then comes complete with all software and hardware requirements needed for deployment. After the model deployment phase, users have several options to access details behind model predictions.
As a result, users at all levels get a single development environment. They can access the entire ML workflow from one central point. From here, they can reduce the complexity of model maintenance and accelerate models into production.
Pre-Trained APIs
Vertex AI comes complete with pre-trained application programming interfaces (APIs) for some of the more common use cases. These include translation or speech-to-text applications, but they also work extremely well for image- and video-based datasets.
If your datasets fall into one of those categories, you can train models with minimal ML knowledge.
Vertex AI provides a unified set of APIs for the ML lifecycle. Diagram courtesy Henry Tappen and Brian Kobashikawa
Seamless Data-to-AI Integration
We have already mentioned the seamlessness of the Vertex AI workbench. Through the platform’s dashboard, users can very quickly access the likes of BigQuery, Dataproc, or Spark. The workbench allows you to export entire datasets from BigQuery and run them in Vertex AI.
Vertex AI data labeling feature helps you generate accurate labels for your data collection.
Support for All Open-Source Frameworks
The platform also supports developers who are looking to write their own machine learning models. It integrates easily with widely-used open-source frameworks such as TensorFlow or PyTorch. In addition, Vertex AI also has custom containers for predictions and training. Built-in integrations allow for faster model selection and model monitoring.
Benefits of Vertex AI
Vertex AI delivers three key features and benefits:
Ability to train models with minimal expertise thanks to pre-trained APIs for vision, language, conversation, and structured data.
Ability to accelerate models to deployment through optimized AI infrastructure without a loss in predictive accuracy.
Ability to manage all your models with confidence through tools like Google Cloud’s Vertex AI pipelines and the Vertex AI feature store.
Building Efficiencies for Infrastructure
Creating an efficient environment for AI and ML model training can be challenging. Vertex AI makes it easy by providing a fully managed platform and taking away the need to administer physical infrastructure.
In practice, that means developers can build without worrying about servers and the costs related to their project. Rather than dealing with costs spiraling out of control, developers will simply be charged for the resources they use. For example, Vertex will choose a standard machine type. If you need more computing power, you can dedicate different resources, including virtual machines, to the project. Workbench cloud storage is accessible based on your needs through flexible Google cloud storage.
As developers are building their central repository of code samples, they do not need to worry about running out of capacity.
The platform also takes care of queuing and logging jobs, as well as monitoring models, leaving developers to concentrate on the ML they are building without distractions.
Perfect for Beginners and Experts
Vertex AI is one of the few platforms that cater to the needs of ML beginners and offers tools sophisticated enough to capture the interest of expert developers.
While those relatively new to ML can take advantage of pre-trained APIs for some of the most common ML tasks, more experienced developers will find the platform to be compatible with open-source frameworks. This level of flexibility accommodates as much custom code as a developer wants to write without making it inaccessible to those with less experience.
Even if developers need to step away from their models for a certain time, the Vertex AI model registry allows them to store current models and training jobs to return to later. For more experienced developers, Vertex AI TensorBoard offers an enterprise-ready, managed version of TensorBoard.
Helps Solving Increasingly Complex Tasks
Vertex AI manages what previous platforms struggled with. It manages complex problems, such as running billions of iterations of a model to determine the best algorithm.
Google believes that its unified approach to ML development allows users to speed up ML development and experimentation by 5%. In addition, it reduces the lines of code needing to be written by 80%.
Plus, one of its greatest achievements is making these complex tasks accessible to more people. Users do not need to be data scientists to use the platform. According to Google, this democratic approach is already showing results with 2.5 times more Vertex AI predictions generated in 2021 than in 2020.
Preventing unauthorized access to ML projects, models in production and the data used to train is high on the list of priorities for many users. The Vertex AI workbench is equipped with user-managed notebooks instances that are protected by Google Cloud authorization and authentication.
These managed notebooks instances are pre-packaged and set up within a Jupyter notebook-based working environment. Jupiter notebooks support GPU accelerators and will also sync with GitHub’s data repositories.
The notebook environment can be customized to use built-in integrations or you can choose to control notebooks cells through the user-managed notebooks option. Both options, including the user-managed notebooks instance, are pre-programmed, allowing developers to choose their preferred notebook execution.
Get Started Using Google Vertex AI
Are you ready to optimize your AI and ML deployment? The Google team saw a 25% increase in active customers using Vertex AI in the first six months of 2022, and the trend looks set to continue.
Use cases cover retail, payment processing, and manufacturing, with big-name brands like Coca-Cola taking advantage of the technology. Financial services company Cash App managed to speed up the completion time for some of its core ML processing workflows by 66%.
Getting started with Vertex AI is straightforward. Once a user has decided which training method they would like to apply for their online predictions project, all that is required is migrating resources and applications to Vertex AI before setting up a project.
References on Vertex AI
Because Vertex AI is aimed at non-expert users as well as experts, Google provides an extensive range of written guides and video-based introductions and instructions.
The documents make it easy for non-experts to get started and understand the importance of each individual step. As the support information becomes gradually more specific, expert users will find the information they’re looking for.
Google has been setting standards for ML development for some time. While its Google Colab platform is aimed at data scientists and developers, Vertex AI predictions are accessible to anyone with basic knowledge in the field. Accelerating a model into production is only one benefit. Vertex AI prediction has also made app development more accessible for people outside of data science, which has been welcomed by developer advocates.
Introduction
Vertex AI is a cloud-based machine learning platform developed by Google Cloud that provides an end-to-end workflow for building, training, and deploying machine learning models. It supports various types of machine learning tasks, offers tools for data preprocessing and analysis, and includes pre-trained models for common use cases. Vertex AI eliminates the need for infrastructure management, allowing developers, data scientists, and researchers to focus on their machine learning tasks. With Vertex AI, users can train and deploy models on Google Cloud infrastructure, including AI Platform, Kubernetes, and AutoML.
Taking machine learning (ML) applications from being merely an idea to becoming a fully-fledged product used to be the domain of data scientists. Google Cloud’s Vertex AI aims to make the entire process accessible without the need to code.
The goal is to open up ML programming to a wider range of users aside from machine learning engineers. Plus, improved workflow and an easy-to-access user interface should accelerate the work of data scientists, too. In this article, we take a closer look.
Table of contents
Also Read: Google’s Chimera Painter: Using AI to Create Fantastic Creatures
What is Vertex AI?
The aim of Vertex AI is to bring together machine learning tools on one platform. Rather than having to search for and access different Google Cloud platforms on various machine learning platforms at different stages of the workflow, developers find everything in one place.
Ingesting data, training machine learning models, and deploying the most successful models in user-friendly apps can all be achieved from one single platform. Plus, Vertex AI’s single interface makes it easy for novice developers and those without extensive coding knowledge to develop the apps their users need.
As new developers build their expertise through Vertex pipelines, seasoned developers can reap the benefits of working in a flexible and seamless environment. In both cases, app development becomes a faster, smoother process.
A Unified Machine Learning Workflow
To understand the benefits of Vertex AI better, it is worth taking a closer look at a typical ML workflow.
Once developers have chosen a task for which they are looking to create a machine learning-based app, they need to ingest, analyze, and transform raw data. Next is creating and training a specific model to perform the task in question. Training models can often be the most time-consuming part of the process.
After that follows the evaluation stage. Here, the model may be proven to be reliable, but it could also show problems. If developers are not happy with their original model, they may choose to use another one and repeat these two stages.
Once the team has found a reliable model, they can deploy their app and run prediction tasks.
Vertex AI simplifies the entire data science workflow right from the beginning. Managed datasets support the initial data preparation stages. Data can also be labeled and annotated right on the platform. There is simply no need to switch between different services.
At the Vertex AI training stage, Auto ML is available to take care of image files, videos, text-based data, and tables. If your data is stored in these formats, there is no need to create a custom model. Vertex AI will simply select the most suitable model for prediction.
At the same time, developers do not need to compromise on insights. Vertex ML metadata allows them to record parameters and observations of an experiment. Vertex ML metadata also helps track model artifacts. Feature attributions allow users to look at Vertex AI predictions in even more detail. Feature attributions help you see which features or training code contributed most to the outcome, forming the basis of further feature engineering.
For other applications, or if developers want more control, they can use custom-trained models from frameworks and their optimal model architectures instead of pre-trained models. To facilitate this, Vertex provides Docker container images as part of its training service.
Vertex explainable AI lets you understand the reasoning behind your model’s predictions at the evaluation stage. Vertex AI then comes complete with all software and hardware requirements needed for deployment. After the model deployment phase, users have several options to access details behind model predictions.
As a result, users at all levels get a single development environment. They can access the entire ML workflow from one central point. From here, they can reduce the complexity of model maintenance and accelerate models into production.
Pre-Trained APIs
Vertex AI comes complete with pre-trained application programming interfaces (APIs) for some of the more common use cases. These include translation or speech-to-text applications, but they also work extremely well for image- and video-based datasets.
If your datasets fall into one of those categories, you can train models with minimal ML knowledge.
Seamless Data-to-AI Integration
We have already mentioned the seamlessness of the Vertex AI workbench. Through the platform’s dashboard, users can very quickly access the likes of BigQuery, Dataproc, or Spark. The workbench allows you to export entire datasets from BigQuery and run them in Vertex AI.
Vertex AI data labeling feature helps you generate accurate labels for your data collection.
Support for All Open-Source Frameworks
The platform also supports developers who are looking to write their own machine learning models. It integrates easily with widely-used open-source frameworks such as TensorFlow or PyTorch. In addition, Vertex AI also has custom containers for predictions and training. Built-in integrations allow for faster model selection and model monitoring.
Benefits of Vertex AI
Vertex AI delivers three key features and benefits:
Building Efficiencies for Infrastructure
Creating an efficient environment for AI and ML model training can be challenging. Vertex AI makes it easy by providing a fully managed platform and taking away the need to administer physical infrastructure.
In practice, that means developers can build without worrying about servers and the costs related to their project. Rather than dealing with costs spiraling out of control, developers will simply be charged for the resources they use. For example, Vertex will choose a standard machine type. If you need more computing power, you can dedicate different resources, including virtual machines, to the project. Workbench cloud storage is accessible based on your needs through flexible Google cloud storage.
As developers are building their central repository of code samples, they do not need to worry about running out of capacity.
The platform also takes care of queuing and logging jobs, as well as monitoring models, leaving developers to concentrate on the ML they are building without distractions.
Perfect for Beginners and Experts
Vertex AI is one of the few platforms that cater to the needs of ML beginners and offers tools sophisticated enough to capture the interest of expert developers.
While those relatively new to ML can take advantage of pre-trained APIs for some of the most common ML tasks, more experienced developers will find the platform to be compatible with open-source frameworks. This level of flexibility accommodates as much custom code as a developer wants to write without making it inaccessible to those with less experience.
Even if developers need to step away from their models for a certain time, the Vertex AI model registry allows them to store current models and training jobs to return to later. For more experienced developers, Vertex AI TensorBoard offers an enterprise-ready, managed version of TensorBoard.
Helps Solving Increasingly Complex Tasks
Vertex AI manages what previous platforms struggled with. It manages complex problems, such as running billions of iterations of a model to determine the best algorithm.
Google believes that its unified approach to ML development allows users to speed up ML development and experimentation by 5%. In addition, it reduces the lines of code needing to be written by 80%.
Plus, one of its greatest achievements is making these complex tasks accessible to more people. Users do not need to be data scientists to use the platform. According to Google, this democratic approach is already showing results with 2.5 times more Vertex AI predictions generated in 2021 than in 2020.
Also Read: How To Get Started With Machine Learning In Julia
Reduces Risk for Production Deployments
Preventing unauthorized access to ML projects, models in production and the data used to train is high on the list of priorities for many users. The Vertex AI workbench is equipped with user-managed notebooks instances that are protected by Google Cloud authorization and authentication.
These managed notebooks instances are pre-packaged and set up within a Jupyter notebook-based working environment. Jupiter notebooks support GPU accelerators and will also sync with GitHub’s data repositories.
The notebook environment can be customized to use built-in integrations or you can choose to control notebooks cells through the user-managed notebooks option. Both options, including the user-managed notebooks instance, are pre-programmed, allowing developers to choose their preferred notebook execution.
Get Started Using Google Vertex AI
Are you ready to optimize your AI and ML deployment? The Google team saw a 25% increase in active customers using Vertex AI in the first six months of 2022, and the trend looks set to continue.
Use cases cover retail, payment processing, and manufacturing, with big-name brands like Coca-Cola taking advantage of the technology. Financial services company Cash App managed to speed up the completion time for some of its core ML processing workflows by 66%.
Getting started with Vertex AI is straightforward. Once a user has decided which training method they would like to apply for their online predictions project, all that is required is migrating resources and applications to Vertex AI before setting up a project.
References on Vertex AI
Because Vertex AI is aimed at non-expert users as well as experts, Google provides an extensive range of written guides and video-based introductions and instructions.
The documents make it easy for non-experts to get started and understand the importance of each individual step. As the support information becomes gradually more specific, expert users will find the information they’re looking for.
Also Read: Siri vs. Alexa vs. Cortana: Comparing Virtual Personal Assistants
Conclusion
Google has been setting standards for ML development for some time. While its Google Colab platform is aimed at data scientists and developers, Vertex AI predictions are accessible to anyone with basic knowledge in the field. Accelerating a model into production is only one benefit. Vertex AI prediction has also made app development more accessible for people outside of data science, which has been welcomed by developer advocates.
References
“Get Started.” Google Cloud, https://cloud.google.com/vertex-ai/docs/start. Accessed 18 Mar. 2023.
Mahendra, Sanksshep. “Democratizing Artificial Intelligence.” Artificial Intelligence +, 28 Feb. 2023, https://www.aiplusinfo.com/blog/democratizing-artificial-intelligence/. Accessed 18 Mar. 2023.
Tappen, Henry. “How Businesses Use Google Cloud VertexAI.” Google Cloud, 9 June 2022, https://cloud.google.com/blog/products/ai-machine-learning/how-businesses-use-google-cloud-vertex-ai. Accessed 18 Mar. 2023.
Tech, Google Cloud. “What Is Vertex AI?” YouTube, Video, 22 May 2021, https://www.youtube.com/watch?v=gT4qqHMiEpA. Accessed 18 Mar. 2023.
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