Over the past few years, Google has introduced many easy-to-use tools to help data scientists and machine learning engineers. Google Colab, TensorFlow, BigQueryML, Cloud AutoML, Cloud AI, and similar other tools have been introduced by Google Cloud to make AI more accessible to organizations.
However, when there are so many AI tools readily available, it becomes a tedious job to use different tools for performing data analysis, models training, deployment, etc. To unify this process, Google has introduced "Vertex AI", which makes all its cloud offerings available under one roof. Let’s have a look at what exactly is Vertex AI, its features, and its use-cases!
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What is Vertex AI?
Vertex AI is a unified artificial intelligence platform that offers all of Google’s cloud services under one roof. With Vertex AI, you can build ML models or deploy and scale them easily using pre-trained and custom tooling. When you develop ML solutions on Vertex AI, you can leverage AutoML and other advanced ML components to greatly enhance productivity and scalability.
Google also focused to make Vertex AI a friendly platform for newbies and a time-saving solution for experts. That’s the reason it can train models easily and requires 80% fewer lines of coding.
Key Features of Vertex AI
Although Vertex AI has tons of features available, here’s the look at some of its key offerings:
- Entire ML workflow under one unified UI: Vertex AI provides one unified user interface and API for all AI-related Google Cloud services. For example, within Vertex AI, you can use AutoML to train and compare models and store them in a central model repository.
- Integrates with all open source frameworks: Vertex AI integrates with commonly used open source frameworks, such as PyTorch and TensorFlow, and it also supports other tools via custom containers.
- Access to pre-trained APIs for video, vision, and others: Vertex AI makes it easy to integrate video, translation, and natural language processing with existing applications. AutoML empowers engineers to train models customized to meet their business needs with minimal expertise and effort.
- End-To-End data and AI integration: Vertex AI is integrated with Dataproc, Dataflow and BigQuery natively through Vertex AI Workbench. You can either build/run ML models in BigQuery or you can use export data from BigQuery to Vertex AI Workbench and execute ML models from there.
Use Cases of Vertex AI
Data scientists and ML engineers can take advantage of Vertex AI in many ways. What are the use cases Vertex AI? You can:
- Insert data from Cloud Storage and Big Query and use Vertex AI Data Labeling to improve prediction accuracy and interpret high-quality training data.
- Use Vertex AI Feature Store (fully-managed feature-rich repository) to serve, reuse, and share ML features.
- Use Vertex AI Pipelines to streamline the development and execution of ML processes.
- Use Vertex AI Prediction to streamline the deployment and monitoring of models to production.
- Use Vertex Explainable AI to get comprehensive model feature attributions and evaluation metrics.
Furthermore, the managed APIs (Vision, Video, NLP…) make it easy for teams without in-depth ML knowledge or dedicated machine learning engineers to add AI capabilities to their applications.
In a nutshell, Vertex AI is an all-in-one platform for data scientists offering every single tool they need to manage, develop, deploy, interpret, and monitor the models. Without any formal ML training, newbies and experts can quickly start using Vertex AI right away.
Would you like to learn more? Check out this explanatory video by Priyanka Vergadia, Lead Developer Advocate at Google.