New Era of Datascience in the Cloud

In 2021 the way corporate clients do datascience in the Cloud is changing. This is an AI-as-a-Service story. Vertex AI is Google's fundamental redesign of its automated machine learning stack, or AI as a Service for its Cloud clients.

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Back to Vertex AI. Companies can build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified AI platform.  In addition to integrating the individual components of the stack more closely together, Vertex AI also introduces new tools to help data teams monitor the models they put into production, as Google Cloud makes a push into MLOps.

This as it seeks to take market share away from AWS and Azure. Google thinks it can manage the machine learning model deployment lifecycle better than its rivals. Google Cloud may be just as guilty as others in the industry of providing a series of disconnected capabilities in data science. But with Vertex AI now that's about to change.

A Unified UI and API

Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository.

At the recent Google I/O 2021 conference, the cloud provider announced the general availability of Vertex AI, a managed machine learning platform designed to accelerate the deployment and maintenance of artificial intelligence models.

So what Can it Do?

Pre-trained APIs for vision, video, natural language, and more

Easily infuse vision, video, translation, and natural language ML into existing applications or build entirely new intelligent applications across a broad range of use cases (including Translation and Speech to Text). AutoML enables developers to train high-quality models specific to their business needs with minimal ML expertise or effort. With central managed registry for all datasets across data types (vision, natural language, and tabular).

End-to-end integration for data and AI

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 for seamless integration across the data-to-AI life cycle. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

Support for all open source frameworks

Vertex AI integrates with widely used open source frameworks such as TensorFlow, PyTorch, and scikit-learn, along with supporting all ML frameworks via custom containers for training and prediction.

Essentially the short answer is using Vertex AI, engineers can manage image, video, text, and tabular datasets, and build machine learning pipelines to train and evaluate models using Google Cloud algorithms or custom training code. They can then deploy models for online or batch use cases all on scalable managed infrastructure.

That sounds pretty useful, well done Google Cloud. See features here.

What else? The new service provides Docker images that developers run for serving predictions from trained model artifacts, with prebuilt containers for TensorFlow, XGBoost and Scikit-learn prediction. If data needs to stay on-site or on a device, Vertex ML Edge Manager, currently experimental, can deploy and monitor models on the edge.

Vertex AI is partly a re-brand but also replaces legacy services such as AI Platform Data Labeling, AI Platform Training and Prediction, AutoML Natural Language, AutoML Video, AutoML Vision, AutoML Tables, and AI Platform Deep Learning Containers.

Vertex AI is likely a rebranding of AI Platform (Unified) which was in beta for the last year or so. Google can be a bit sneaky in this manner. In a separate article, Google explains how to streamline ML training workflows with Vertex AI, avoiding running model training on local environments like notebook computers or desktops and working instead with Vertex AI custom training service. Vertex AI certainly does democratize AI to some degree giving AI-as-a-Service capabilities to even people without prior training.

The grand plan with Vertex AI, according to Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud, is to “get data scientists and engineers out of the orchestration weeds, and create a industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.”

As how we do AI in the Cloud changes, the Cloud is also innovating in other new ways. Alibaba launched a cloud-based livestreaming product designed for e-commerce recently. China is significantly ahead of the West in social commerce, convenience of payments and E-commerce diversified options for consumers.

Alibaba Cloud really is the fourth horseman. Cloud computing is seen as a key profit driver for Alibaba over the long-term and over the past few years it has been boosting its presence aggressively outside of China. AI and E-commerce innovations means cloud adoption will continue at a rapid pace. Livestream shopping usually involves a host talking about products that customers can buy directly via the live broadcast and is more popular in Asian countries where often mobile devices are key to how people shop.

In the Asia-Pacific region, Alibaba was the biggest public cloud market vendor at the end of 2020 with a 19.2% share, according to IDC, boosted by success in China. Meanwhile for Google Cloud, it grew its revenue 46% to $4B in Q1 (late April, 2021).