Google AI Platform vs Google Anthos

Overview

Google Anthos

Stacks54

Followers266

Votes8

Google AI Platform

Stacks49

Followers119

Votes0

Google AI Platform vs Google Anthos: What are the differences?

Google AI Platform: Create your AI applications once, then run them easily on both GCP and on-premises. Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively; Google Anthos: Bringing the Google Cloud to you. Formerly Cloud Services Platform, Anthos lets you build and manage modern hybrid applications across environments. Powered by Kubernetes and other industry-leading open-source technologies from Google.

Google AI Platform can be classified as a tool in the "Machine Learning as a Service" category, while Google Anthos is grouped under "Platform as a Service".

Some of the features offered by Google AI Platform are:

“No lock-in” flexibility
Supports Kubeflow
Supports TensorFlow

On the other hand, Google Anthos provides the following key features:

Google Kubernetes Engine Support
GKE On-Prem Support
Istio on GKE Support

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Detailed Comparison

Google Anthos	Google AI Platform
Formerly Cloud Services Platform, Anthos lets you build and manage modern hybrid applications across environments. Powered by Kubernetes and other industry-leading open-source technologies from Google.	Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.
Google Kubernetes Engine Support; GKE On-Prem Support; Istio on GKE Support; Anthos Config Management; Stackdriver Support; Kubernetes applications on GCP Marketplace; Serverless; API management; Continuous integration; Continuous delivery	“No lock-in” flexibility; Supports Kubeflow; Supports TensorFlow; Supports TPUs; Build portable ML pipelines; on-premises or on Google Cloud; TFX tools
Statistics
Stacks 54	Stacks 49
Followers 266	Followers 119
Votes 8	Votes 0
Pros & Cons
Pros 3 Operations support by Google SRE 2 Host Cloud Run (managed knative) anywhere 1 Access to Google Kubernetes Marketplace 1 Automatic k8s upgrades 1 Policy enforcement via ACM Cons 3 Expensive	No community feedback yet
Integrations
Google Kubernetes Engine Elasticsearch MongoDB GitLab Istio Docker Kubernetes Couchbase Splunk Neo4j	Google Cloud Storage Google BigQuery TensorFlow Google Cloud Dataflow Kubeflow

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