Key services provided by Google Cloud Platform (GCP). How its different from competitors.

Key services provided by Google Cloud Platform (GCP) differentiate it from its competitors like Microsoft Azure and Amazon Web Services (AWS).

Here are some examples:

1. BigQuery: This is a fully-managed, serverless data warehouse service that allows users to analyze large datasets in real time. BigQuery offers advanced data analytics capabilities, including machine learning integration, and is known for its fast query performance and scalability.

2. Machine Learning Engine: This is a managed machine learning service that allows users to train, deploy, and manage machine learning models using popular frameworks like TensorFlow Enterprise and Scikit-Learn. It provides an integrated, end-to-end machine-learning workflow that simplifies the process of building and deploying machine-learning models.

3. Vertex AI: Provides a unified and scalable environment for building, training, and deploying machine learning models. It aims to simplify the process of developing and deploying machine learning models by offering a range of tools and services that streamline the end-to-end machine learning workflow. With Vertex AI, users can easily manage and automate various stages of the machine learning pipeline, including data preparation, model training, evaluation, deployment, and monitoring. It provides a collaborative environment for data scientists, machine learning engineers, and developers to work together on machine learning projects.

a. AutoML: This is a suite of machine learning products that allow users to build custom machine learning models with little or no coding required. It includes AutoML Vision, AutoML Natural Language, AutoML Tables, and AutoML Translation, which offer pre-trained models and customization options for various machine-learning tasks.

Note: All the functionality of legacy AutoML and new features are available on the Vertex AI platform. See Migrate to Vertex AI to learn how to migrate your resources.

b. AI Platform: This is a comprehensive set of tools and services for building, training, and deploying machine learning models at scale. It includes features like Kubeflow Pipelines for building end-to-end ML workflows, AI Platform Notebooks for collaborative Jupyter notebooks, and AI Platform Prediction for serving ML models.

Note: Vertex AI is the next generation of AI Platform, with many new features that are unavailable in the AI Platform.

4. Spanner: Cloud Spanner delivers industry-leading high availability (99.999%) for multi-regional instances—10x less downtime than four nines—and provides transparent, synchronous replication across the region and multi-region configurations. This is a globally-distributed, horizontally-scalable relational database service that provides strong consistency and high availability. Spanner is designed to handle large-scale, mission-critical workloads and offers features like automatic scaling, automated backups, and global replication for high performance and reliability.

5. IoT Core: This is a fully-managed service for connecting, managing, and ingesting data from Internet of Things (IoT) devices at scale. It provides features like device management, data ingestion, and device provisioning, and integrates with other GCP services like Cloud Pub/Sub, Cloud Storage, and BigQuery for data processing and analysis.

Note: Google Cloud IoT Core is being retired on August 16, 2023.

6. Vision and Video AI: These are machine learning-based services that provide advanced image and video analysis capabilities, including object detection, facial recognition, and content moderation. They are designed for applications like image recognition, video surveillance, and content moderation, and offer pre-trained models as well as customization options.

These are just some examples of the key services provided by GCP that differentiate it from its competitors like Azure and AWS. GCP offers a wide range of other services across various domains, including compute, storage, networking, security, analytics, and more, that cater to different needs and requirements of businesses and developers.

Points to remember:

GCE and GCP are two different things. GCE stands for Google Compute Engine which is part of Google’s Infrastructure-as-a-Service (IaaS) offering. It allows you to build high-performance, fault-tolerant, massively scalable compute nodes to handle your application’s needs. On the other hand, GCP stands for Google Cloud Platform which offers multiple services like GCE, Google Kubernetes Engine (GKE), Google App Engine (GAE), and Google Cloud Functions (GCF). It’s a secure and customizable compute service that lets you create and run virtual machines on Google’s infrastructure.

DISCLAIMER: It's always recommended to refer to the official documentation and websites of GCP, Azure, AWS, or any other cloud service provider for the most current and accurate information. Additionally, decisions regarding the selection and use of cloud services should be made based on thorough research, consideration of specific requirements, and consulting with appropriate experts or professionals.