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Google AI Platform
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Google AI Platform vs Google App Engine: 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 App Engine: Build web applications on the same scalable systems that power Google applications. Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.

Google AI Platform can be classified as a tool in the "Machine Learning as a Service" category, while Google App Engine 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 App Engine provides the following key features:

  • Zero to sixty: Scale your app automatically without worrying about managing machines.
  • Supercharged APIs: Supercharge your app with services such as Task Queue, XMPP, and Cloud SQL, all powered by the same infrastructure that powers the Google services you use every day.
  • You're in control: Manage your application with a simple, web-based dashboard allowing you to customize your app's performance.
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What is Google AI Platform?

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.

What is Google App Engine?

Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
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        What are some alternatives to Google AI Platform and Google App Engine?
        Azure Machine Learning
        Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
        Amazon Machine Learning
        This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
        Amazon SageMaker
        A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
        NanoNets
        Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
        Amazon Elastic Inference
        Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
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        Reviews of Google AI Platform and Google App Engine
        Review ofGoogle App EngineGoogle App Engine

        With Cloud Endpoints you can create and deploy mobile backend in one hour or less. And it is free (until you need extra scale). I would not recommend to use Java - python is faster and has all new appengine features.

        Pros: everything is in one place: task queue, cron, backend instances for data processing, datastore, mapreduce. Cons: you cannot easily move your code from GAE. Even with special 3rd party services.

        Review ofGoogle App EngineGoogle App Engine

        With Cloud Endpoints you can create and deploy mobile backend in one hour or less.

        How developers use Google AI Platform and Google App Engine
        Avatar of Casey Smith
        Casey Smith uses Google App EngineGoogle App Engine

        PaaS for back-end components, including external data ingestion APIs, front-end web service APIs, hosting of static front-end application assets, back-end data processing pipeline microservices, APIs to storage infrastructure (Cloud SQL and Memcached), and data processing pipeline task queues and cron jobs. Task queue fan-out and auto-scaling of back-end microservice instances provide parallelism for high velocity data processing.

        Avatar of Lawrence Cheuk
        Lawrence Cheuk uses Google App EngineGoogle App Engine

        checking a swap require a lot of cpu resource, roster normally come out same day of month, every month, at a particular time. Which make very high spike, our flag ship product, iSwap, with the capability looking swap possibility with 10000 other rosters base on user critieria, you need a cloud computing give you this magnitude of computing power. gae did it nicely, user friendly, easy to you, low cost.

        Avatar of CommentBox.io
        CommentBox.io uses Google App EngineGoogle App Engine

        App engine fills in the gaps in the increasingly smaller case where it's necessary for us to run our own APIs.

        Avatar of Abhijeet Gokar
        Abhijeet Gokar uses Google App EngineGoogle App Engine

        Very easy to make cloud computing of ML models , and use containers like Kubernetes.

        Avatar of Vamsi Krishna
        Vamsi Krishna uses Google App EngineGoogle App Engine

        Cloud instances to run our app, Cloud MySQL , Network Load Balancer

        How much does Google AI Platform cost?
        How much does Google App Engine cost?
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