Google Cloud ML Engine has been evolved and rebranded as part of Google Cloud’s AI Platform. The AI Platform is a suite of services that allows users to build, train, and deploy machine learning models on Google Cloud Platform (GCP). It’s designed to provide a flexible and scalable infrastructure for machine learning and AI projects.

Key Features of AI Platform (formerly Google Cloud ML Engine)

1. Model Building and Training:

   • Offers a managed service to build and train machine learning models at scale.
   • Supports popular machine learning frameworks like TensorFlow, PyTorch, and scikit-learn.
   • Allows for both automatic and custom model training.

2. Model Deployment and Prediction:

   • Easy deployment of trained models for obtaining predictions.
   • Provides both batch and online prediction services.
   • Supports automatic scaling of machine learning models based on demand.

3. Integrated Tooling:

   • Seamless integration with other GCP services like BigQuery, Dataflow, Cloud Storage, and Pub/Sub for end-to-end machine learning workflows.
   • Integration with Jupyter notebooks through AI Platform Notebooks for interactive development and analysis.

4. Hyperparameter Tuning:

   • Automatically tunes hyperparameters in a model to optimize performance.

5. Pre-built and Custom Containers:

   • Supports the use of pre-built containers for machine learning frameworks and the ability to bring custom containers.

6. ML Pipelines:

   • Enables the creation of reusable, scalable, and manageable machine learning pipelines using TensorFlow Extended (TFX) and Kubeflow Pipelines.

Use Cases

• Custom Model Training and Deployment: Train custom models on a managed infrastructure and deploy them for predictions.
• Data Science and Analysis: Perform data analysis and model development interactively using AI Platform Notebooks.
• Automated Machine Learning: Utilize AutoML capabilities for training high-quality models with minimal effort.

Getting Started

1. Set Up a GCP Account: To use AI Platform, you need a Google Cloud account and a project with billing enabled.

2. Data Preparation: Prepare and store your data in GCP, using services like BigQuery or Cloud Storage.

3. Model Development and Training:

   • Develop your machine learning model using AI Platform Notebooks or your preferred environment.
   • Train your model on AI Platform, leveraging its scalable infrastructure.

4. Model Deployment:

   • Once the model is trained, deploy it on AI Platform for obtaining predictions.
   • Choose between online and batch prediction services based on your needs.

5. Monitoring and Management:

   • Use GCP’s monitoring tools to track the performance and health of your deployed models.

Conclusion

The AI Platform, an evolution of Google Cloud ML Engine, provides a comprehensive and integrated environment for machine learning and AI projects, making it easier for data scientists and developers to build, train, and deploy machine learning models at scale on the cloud. With its support for popular ML frameworks and seamless integration with other GCP services, AI Platform is well-suited for a wide range of machine learning tasks, from experimentation to production deployment.