Review: Google Cloud AI lights up machine learning

Google has one of the largest machine learning stacks in the industry, currently centering on its Google Cloud AI and Machine Learning Platform. Google spun out TensorFlow as open source years ago, but TensorFlow is still the most mature and widely cited deep learning framework. Similarly, Google spun out Kubernetes as open source years ago, but it is still the dominant container management system.

Google is one of the top sources of tools and infrastructure for developers, data scientists, and machine learning experts, but historically Google AI hasn’t been all that attractive to business analysts who lack serious data science or programming backgrounds. That’s starting to change.

The Google Cloud AI and Machine Learning Platform includes AI building blocks, the AI platform and accelerators, and AI solutions. The AI solutions are fairly new and aimed at business managers rather than data scientists. They may include consulting from Google or its partners.

The AI building blocks, which are pre-trained but customizable, can be used without intimate knowledge of programming or data science. Nevertheless, they are often used by skilled data scientists for pragmatic reasons, essentially to get stuff done without extensive model training.

The AI platform and accelerators are generally for serious data scientists, and require coding skill, knowledge of data preparation techniques, and lots of training time. I recommend going there only after trying the relevant building blocks.

There are still some missing links in Google Cloud’s AI offerings, especially in data preparation. The closest thing Google Cloud has to a data import and conditioning service is the third-party Cloud Dataprep by Trifacta; I tried it a year ago and was underwhelmed. The feature engineering built into Cloud AutoML Tables is promising, however, and it would be useful to have that sort of service available for other scenarios.

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