AI Platform Overview Google Professional Data Engineer GCP

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  • The diagram shows high-level overview of ML workflow stages.
  • The blue-filled boxes indicate where AI Platform provides managed services and APIs:


AI Platform can manage the following stages in the ML workflow:

  • Train an ML model on data:
    • Train model
    • Evaluate model accuracy
    • Tune hyperparameters
  • Deploy trained model.
  • Send prediction requests to model:
    • Online prediction
    • Batch prediction (for TensorFlow only)
  • Monitor the predictions on an ongoing basis.
  • Manage models and model versions.

Components of AI Platform

  • A Training service:
  • allows you to train models using a wide range of different customization options.
  • can select
    • many different machine types
    • power training jobs,
    • enable distributed training, use hyperparameter tuning,
    • accelerate with GPUs and TPUs.
  • can select different ways to customize training application.
  • can submit input data for AI Platform to train


Prediction service:

  • allows to serve predictions based on a trained model,
  • whether or not the model was trained on AI Platform.



  • enables you to create and manage VM instances
  • Instances are pre-packaged with JupyterLab.
  • Also has deep learning packages, like TensorFlow and PyTorch
  • can configure either CPU-only or GPU-enabled instances
  • instances are protected by Google Cloud authentication and authorization
  • can easily sync notebook with a GitHub repository.


Data labeling service:

  • lets you request human labeling for a dataset to use to train a custom machine learning model.
  • You can submit a request to label video, image, or text data.
  • provide a representative sample of labeled data, specify all possible labels for dataset, and provide some instructions for how to apply those labels.



Deep learning VM image:

  • It lets you choose from a set of Debian 9-based machine images
  • optimized for data science and machine learning tasks.
  • All come with key ML frameworks and tools pre-installed
  • can be used on instances with GPUs


AI Platform Deep Learning Containers

  • are a set of Docker containers
  • have data science frameworks, libraries, and tools pre-installed.

Deep Learning Containers images include the following:


  • TensorFlow
  • TensorFlow 2.0
  • PyTorch
  • scikit-learn
  • R

Python, including these packages:

  • numpy
  • sklearn
  • scipy
  • pandas
  • nltk
  • pillow
  • many others

Nvidia packages with the latest Nvidia driver for GPU-enabled instances:

  • CUDA 10.0
  • CuDNN 7.*
  • NCCL 2.*




Tools to interact with AI Platform

Google Cloud Console:

  • deploy models to the cloud and manage models, versions, and jobs on the Cloud Console.
  • gives a UI for working with machine learning resources.


The gcloud command-line tool:

  • manage models and versions, submit jobs, and accomplish other AI Platform tasks
  • recommended for most AI Platform tasks



  • provides RESTful services for managing jobs, models, and versions, and for making predictions
  • use the Google APIs Client Library for Python to access the APIs.