Some of the popular offerings from Google would include TensorFlow and Google Colab. To go a step further, tools like Cloud AI, Cloud AutoML, BigQueryML were also introduced to the Google Cloud Platform to make things easy for data scientists and machine learning engineers. Considering an arsenal of AI tools available, it is often a process that involves going back and forth to analyze data, train models, deploy to production, and monitor them. With Google’s Vertex AI, all its cloud offerings will be available under a single roof, managed for you.
What is Vertex AI?
Vertex AI is a managed machine learning platform that provides you with all of Google’s cloud services in one place to deploy and maintain AI models. Is everything related to AI and machine learning in Google Cloud Platform? Yes, likely. Google makes sure that the services keep running to not affect your workflow. So, you do not have to worry about maintaining the infrastructure. Considering it as a “managed” platform, it automates things for you and lets you focus on more important things in a machine learning workflow. With Vertex AI, Google targets the newbies to provide ease of use and the experts to save time for basic tasks. It makes training models way easier, with almost 80% fewer lines of code needed (as they officially claim) compared to other competitive AI platforms like Azure Machine Learning. The official video below gives plenty of technical insights on Vertex AI and how it affects your typical machine learning workflow.
Vertex AI Promotes MLOps
AI has tremendous potential for innovation for businesses. But effectively implementing it is a constant challenge. Hence, MLOps is an approach that is increasingly being encouraged to make things efficient. In case you did not know, MLOps promotes automation to deploy a production-ready model with minimal risks quickly. This also guarantees quality and saves time. Google’s Vertex AI is one platform-as-a-service for machine learning and AI developers to encourage MLOps in the industry.
Google Vertex AI: Feature Overview
If you are a machine learning or AI enthusiast, you probably know what you want to do with Vertex AI. But, to give you a head start, let me highlight the key features of Vertex AI.
A Unified Machine Learning Workflow
Technically, you get to use tools like AutoML, Explainable, Vizier, Edge manager, and more to work on machine learning models. And, all that is accessible from a single platform should be a game-changer. You can also use custom code to train and still have everything you need to be stored in one central repository.
Pre-Trained APIs
Not just making your workflow easy, but to make things faster, you get pre-trained APIs for vision, video, natural language, and more. You can integrate them easily into your existing applications or just spin a new application using that. So, you may not need to look for some of the other AI API platforms to get your work done. If you are working to develop something innovative yet straightforward, Google’s Vertex AI may help you develop the solution in no time, with the pre-trained APIs available. Not just the reduced effort, but even if you have minimal experience, this should help.
Seamless data-to-AI Integration
It’s no surprise that BigQueryML is popularly used to create and execute machine learning models using SQL queries. With Vertex AI, you will be able to access that and export datasets into the platform to integrate it with the workflow. So, you get end-to-end integration.
Support for All Open-Source Frameworks
Every open-source framework is different and yet useful. You may have to adapt to different workflows when deploying a PyTorch and TensorFlow models. And Google Vertex AI supports all those open-source models, including scikit-learn. This helps you approach all the models in the same way, no matter if they are custom coded or made using AutoML.
Benefits of Vertex AI
While MLOps is one, there are a couple of other significant advantages that Vertex AI introduces. Some of the important ones to highlight would be:
Saving Money and Time for Infrastructure
Vertex AI will help you reduce the cost of setting up your own infrastructure (through Kubernetes, for instance) because you pay for what you use. Also, it should significantly reduce the effort to set up or manage your own infrastructure to train machine learning models. So even analysts or researchers with minimal experience can help get some work done.
Perfect for Beginners and Experts
Considering that you get many things done without coding them (or setting them), it becomes an excellent choice for beginners. It offers an easy learning curve to train and deploy machine learning models. That also reminds me of one of our resources that lists some online courses for AI if you are an aspiring learner. And for experts, it saves time by providing pre-trained APIs and access to various managed tools to achieve basic tasks/functions.
Paves the Way to Solve Increasingly Complex Tasks
Google Vertex AI makes the machine learning workflow easy. So anyone dealing with a simple problem will have a quick solution. And this will help more people solve increasingly complex problems as they don’t have to worry about the bare minimum.
Reduces Risk for Production Deployments
Even though this is not a review of Google Vertex AI, it should help reduce the risk of models being ready for deployment. Hence, solutions can be implemented confidently to tackle problems seamlessly.
Get Started Using Google Vertex AI
Before you get started, it is important to note that many tools available are still in preview (subject to changes). So it may not be ready for prime time yet. You get $300 free credits when signing up for the Google Cloud Platform trial to use within 90 days. The pricing rates should be the same for AI platform offerings—you can check out the official pricing page for more details. Head on to the Google Vertex AI product page to get started. And if you are curious, you can explore some initial reviews and tests by some of the professionals working in the industry.
