Google Named A Leader In The Gartner 2020 Magic Quadrant For Cloud AI Developer Services

March 5, 2020

3 min read

The enterprise applications for artificial intelligence and machine learning seem to grow by the day. To take advantage of everything AI/ML technologies have to offer, it’s important to have a platform that supports your needs fully—whether you’re a developer, a data scientist, an analyst, or just interested in AI. But with so many features and services to consider, it can be difficult to sort through it all. This is where analyst reports can provide valuable research to help you get the answers you need.

Today, Gartner named Google a Leader in the Gartner 2020 Magic Quadrant for Cloud AI Developer Services report. This designation is based on Gartner’s evaluation of Google’s language, vision, conversation, and structured data products, including AutoML, all of which we deliver through Google Cloud. Let’s take a closer look at some of Gartner’s findings.

Vision AI for every enterprise use case

You don’t need to be an ML expert to reap the benefits that our AI portfolio offers. Our vision and video APIs, along with AutoML Vision and Video products, let developers of any experience level build perception AI into their applications. These products help you understand and derive insights from your images and videos with industry-leading prediction accuracy in the cloud or at the edge.

Our Computer Vision products provide many features to help you understand your visual content and create powerful custom machine learning models:

Through REST and RPC APIs, the Vision API provides access to pretrained models that are ready to use to quickly classify images.
AutoML Vision automates the training of your own custom machine learning models with an easy-to-use graphical interface. It lets you optimize your models for accuracy, latency, and size, and export them to your application in the cloud, or to an array of devices at the edge.
The Video Intelligence API has pre-trained machine learning models that automatically recognize a vast number of objects, places, and actions in stored and streaming video.
AutoML Video Intelligence lets developers quickly and easily train custom models to classify and track objects within videos, regardless of their level of ML experience.
The What-If Tool, an open-source visualization tool for inspecting any machine learning model, enhances your model’s interpretability, offering insights into how it’s making decisions for AutoML Vision and our data-labeling services.

While powerful pre-trained APIs and custom model creation capabilities are part of meeting all of an enterprise’s ML needs, it’s equally important to be able to deploy these models wherever the business needs them. To that end, our AutoML Vision models can be deployed via container wherever it works best for you: in a virtual private cloud, on-premises, and in our public cloud.

Easier and better custom ML models for your structured data

AutoML Tables enables your entire team of data scientists, analysts, and developers to automatically build and deploy state-of-the-art machine learning models on structured data at a massively increased speed and scale. To create ML models, developers usually need training data that’s as complete and clean as possible. AutoML Tables provides information about and automatically handles missing data, high cardinality, and distribution for each feature in a dataset. Then, in training, it automates a range of feature engineering tasks, from normalization of numeric features and creation of one-hot encoding, to embeddings for categorical features.

In addition, AutoML Tables also provides codeless GUI and python SDK options, as well as automated data preprocessing, feature engineering, hyperparameter and neural/tree architecture search, evaluation, model explainability, and deployment functionality. All of these features significantly reduce the amount of time it takes to bring a custom ML model to production from months to days.

Ready for global scale

As business becomes more and more global, being able to serve customers wherever they are or whatever language they speak is a key differentiator. To that end, many of our products support more languages than other providers. For example: