Google Cloud ML, also referred to as Google Cloud Machine Learning, is an assortment of cutting-edge cloud-based services offered by Google Cloud Platform (GCP) that empower developers to create, train, and deploy machine learning models at scale. By providing an array of tools and services, it simplifies the development and deployment process of machine learning models in the cloud.

Within the realm of Google Cloud ML, you’ll encounter a host of components and features that enhance your machine learning journey:

1. Google Cloud AutoML: A suite of machine learning products that enables users with limited expertise in machine learning to fashion custom models for specific tasks. With AutoML, you gain access to tools tailored for image recognition, natural language processing, and tabular data analysis.

2. Google Cloud AI Platform: This platform offers a comprehensive set of services and tools to create, train, and deploy machine learning models. Its offerings span distributed training, hyperparameter tuning, model serving, and monitoring, ensuring a seamless workflow from start to finish.

3. TensorFlow on Google Cloud: Built by Google, TensorFlow is an open-source machine learning framework. Google Cloud ML provides an optimized version of TensorFlow that facilitates easy deployment and scaling on the cloud. It also integrates smoothly with other Google Cloud services such as BigQuery, Cloud Storage, and Dataflow.

4. Kubeflow on Google Cloud: Kubeflow is an open-source machine learning platform built atop Kubernetes. It empowers users to execute scalable machine-learning workflows and supplies a plethora of tools for experimentation, training, and deployment. Google Cloud ML extends support for Kubeflow within its platform.

5. AI Platform Notebooks: A managed JupyterLab environment that serves as a collaborative space for data exploration, experimentation, and the development of machine learning models. It comes equipped with pre-installed popular machine learning frameworks and libraries, streamlining your workflow.

6. AI Platform Pipelines: This service empowers users to define and deploy machine learning workflows as reusable and reproducible pipelines. Through a visual interface, you can effortlessly design, manage, and monitor intricate machine learning workflows.

These examples merely scratch the surface of the services and tools provided by Google Cloud ML. To facilitate developers, Google Cloud offers extensive documentation, tutorials, and examples, ensuring a smooth onboarding process for machine learning endeavors on their platform.