Sundar Pichai introduced Vertex AI to the world at last year’s Google I/O 2021 conference, competing against managed AI platforms such as Amazon Web Services (AWS) and Azure in the global AI market.
Alphabet CEO once said:
A November 2020 Gartner study found that: Nearly 20% growth rate For managed services such as Vertex AI. Gartner says his public cloud industry growth will sustain through 2024 as companies invest more in mobility and remote collaboration technologies and infrastructure.
Vertex AI replaces legacy services such as AI Platform Training and Prediction, AI Platform Data Labeling, AutoML Natural Language, AutoML Vision, AutoML Video, AutoML Tables, and Deep Learning Containers. Let’s take a look at how the platform has performed and what has changed in the last year.
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What is Google Vertex AI?
Google Vertex AI is a cloud-based third-party machine learning (ML) platform for deploying and maintaining artificial intelligence (AI) models. The Machine Learning Operations (MLOps) Platform blends an automated machine learning (AutoML) and AI platform into a unified application programming interface (API), client library, and user interface (UI).
Previously, data scientists had to run millions of datasets to train algorithms. But now the Vertex technology stack is doing the heavy lifting. It has the computational power to solve complex problems and easily run billions of iterations. Vertex will also come up with the best algorithms for your specific needs.
Vertex AI uses a standard ML workflow consisting of stages such as data collection, data preparation, training, evaluation, deployment, and prediction. Vertex AI has many features, but we’ll take a look at some of the main features here.
- A complete ML workflow under a unified UI umbrella: Vertex AI comes with a unified UI and API for all AI-powered Google Cloud services.
- Integrates with popular open source frameworks: Vertex AI easily blends with commonly used open source frameworks such as PyTorch and TensorFlow, and supports other ML tools through custom containers.
- Access to pre-trained APIs for various datasets: Vertex AI makes it easy to integrate video, image, translation, and natural language processing (NLP) with your existing applications. With minimal expertise and effort, you can train ML models to meet your business needs.
- End-to-end data and AI integration: Vertex AI Workbench lets you natively integrate Vertex AI with Dataproc, Dataflow, and BigQuery. As a result, users can develop or run ML models in BigQuery, or export data from BigQuery and run ML models from Vertex AI Workbench.
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What’s included in the latest update?
At Google, we understand that research is the only way to become an AI-first organization. Many of Google’s products initially Internal research projectof deep mind AlphaFold project led to Running Protein Prediction Models on Vertex AI.
Similarly, neural network research Vertex AI NAS, which allows data science teams to train models with lower latency and power requirements. Empathy therefore plays an important role when considering AI use cases. Some of the latest services within Google’s Vertex AI include:
reduction server
According to Google, an AI training reduction server can be multiple machines, GPUs (graphics processing units), CPUs (central processing units), or custom chips. As a result, you can save time and use fewer resources to complete your training.
Tabular workflow
This feature is intended to customize the ML model creation process. A tabular workflow allows the user to decide which parts of the workflow he wants AutoML technology to handle and which parts he designs himself.
With Vertex AI, you can integrate elements of Tabular Workflow into your existing pipelines. Google has also added state-of-the-art managed algorithms, including advanced research models such as TabNet, advanced algorithms for feature selection, model distillation, and many other capabilities.
Serverless Apache Spark
Vertex AI is integrated with serverless Apache Spark, an integrated open source yet large-scale data analytics engine. Vertex AI users can easily join serverless Spark sessions for interactive code development.
The partnership between Google and Neo4j enables Vertex users to analyze data features on Neo4j’s platform and deploy ML models on Vertex. Similarly, a collaboration between Labelbox and Google has made it possible to access his Labelbox’s data labeling service for various datasets (such as images and text) from the Vertex dashboard.
Explanation based on example
Example-based explanations provide a better solution when data turns into mislabeled data. A new feature in Vertex leverages example-based explanations to diagnose and resolve data issues.
Problem solving with Vertex AI
Google says Vertex AI requires 80% fewer lines of code than other platforms to train AI/ML models with custom libraries, and its custom tools support advanced ML coding claims. Vertex AI’s MLOps tool removes the complexity of self-service model maintenance and streamlines ML pipeline operations and the Vertex Feature Store to deliver, share, and consume advanced ML capabilities.
Data scientists without formal AI/ML training can use Vertex AI, which provides tools for managing data, prototyping, experimenting, and deploying ML models. You can also interpret and monitor AI/ML models in production.
A year after Vertex’s launch, Google is gearing up for real-world applications. The company’s mission is to solve human problems, as featured at Google I/O. This likely means that efforts will be directed toward finding innovative ways to do things through AI.
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