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.
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.