The world of artificial intelligence (AI) and machine learning is growing at an impressive rate. According to research firm Tractica, AI-related revenues will reach $36 billion by 2025, up from $1.5 billion in 2016. However, with so many platforms out there, it can be difficult to find the best cloud computing services to fit your needs and budget. There are various options available, each with its own pros and cons. This article will go over some of the top clouds AI providers so you can make an informed decision about which service works best for you and your machine learning or AI project!
Why cloud?
Cloud computing seems like a natural fit for machine learning (and AI in general). Cloud services allow you to quickly provision, spin down, shut off, and turn on large-scale compute clusters without any downtime. Your data never leaves your company’s servers or IT infrastructure. All of that scales quickly as your business grows. When it comes to AI projects, these advantages can be particularly valuable. For example, some projects might involve training AI models on hundreds of terabytes of structured data—which could take weeks if you were running those workloads on-premises instead of in a cloud environment with massive hardware capacity ready at a moment’s notice. Cloud providers also offer preconfigured AI frameworks and toolsets so developers don’t have to start from scratch when they begin their projects. These features are designed to make AI development faster, easier, and more accessible than ever before.
In addition, cloud providers have developed tools and APIs to help users manage costs. This includes monitoring service usage across different departments, easily setting budgets for individual projects, and alerting developers about spikes in resource usage. Overall, cloud AI platforms represent an exciting new frontier for companies who want to explore what AI has to offer but aren't quite sure where to start.
So let's take a look at five top cloud providers offering dedicated AI solutions today!
IBM Cloud
IBM Cloud is one of the most popular cloud computing platforms in use today. It offers a wide range of services, including analytics, security, IoT, mobile, Watson (AI), blockchain, and more. Developers can build apps with a variety of languages on IBM Cloud’s PaaS (Platform as a Service) platform or its SaaS (Software as a Service) platform. In addition, IBM Cloud also offers private cloud solutions for enterprise customers who want to keep their data on-premises.
Services provided by IBM Cloud for AI and ML:
- Watson Assistant: Watson Assistant is a cloud-based chatbot service that helps you build, train, and deploy conversational chatbots into your applications.
- Watson Discovery: Watson Discovery is a cloud-based natural language processing (NLP) and document retrieval service that helps you extract information from unstructured data.
- Watson Knowledge Studio: Watson Knowledge Studio is a cloud-based service that helps you build, train, and deploy custom machine learning models to extract knowledge from unstructured data.
- Watson Machine Learning: Watson Machine Learning is a cloud-based service that allows you to train, deploy, and manage machine learning models.
- Watson Studio: Watson Studio is a cloud-based IDE that helps you develop, train, and deploy machine learning and deep learning models.
Microsoft Azure
Microsoft Azure is a robust cloud computing platform that is designed to help companies build, deploy, and manage applications on their infrastructure. It offers a wide range of services, including data storage, server management, analytics tools, and much more. Microsoft has been heavily investing in artificial intelligence (AI) and machine learning capabilities in recent years, with many experts predicting it will be one of the biggest winners as AI becomes more pervasive. In fact, according to Gartner’s Magic Quadrant report for 2018, Microsoft was ranked second overall among all major cloud providers in terms of completeness of vision and ability to execute. That said, there are some limitations with using Azure if you’re trying to develop your own custom algorithms or models; it's best suited for use cases where you're using pre-trained models or building something that can run as a web service or mobile app.
Services provided by Microsoft Azure for AI and ML:
- Azure Databricks: Azure Databricks is an Apache Spark-based analytics platform designed to make working with data easier.
- Azure HDInsight: Azure HDInsight is a managed Apache Hadoop service that lets you run Apache Spark, Apache Hive, Apache Kafka, and other big data workloads on a managed cluster.
- Azure Machine Learning: Azure Machine Learning is a cloud-based service that makes it easy to build, deploy, and share predictive models.
- Azure Bot Service: Azure Bot Service is a cloud service that helps you build, deploy, and manage chatbots.
- Azure Cognitive Services: Azure Cognitive Services is a set of APIs that enables you to add AI features to your applications.
Amazon Web Services(AWS)
AWS short for Amazon Web Services is one of the top cloud computing platforms for machine learning and artificial intelligence. With more than a decade in existence, it is one of the most mature cloud computing platforms available. It offers a wide range of services including analytics, machine learning, artificial intelligence, databases, networking tools, and much more. This platform has earned its popularity due to its easy-to-use interface, extensive documentation, and tutorials. Moreover, it supports both big data applications as well as small data applications making it highly versatile. One of its most popular offerings is Amazon Elastic Compute Cloud (EC2) which allows users to run workloads on-demand without having to worry about managing servers or software updates. Another great feature is that you can scale your resources up or down at any time depending on your needs; hence you pay only for what you use making it very cost-effective.
Services provided by AWS for ML and AI:
- Amazon SageMaker: A fully-managed machine learning service that enables developers to build, train, and deploy ML models quickly and easily.
- Amazon Rekognition: A service that makes it easy to add image and video analysis to your applications.
- Amazon Polly: A service that turns text into lifelike speech, allowing you to create applications that talk.
- Amazon Lex: A service that enables you to build conversational interfaces into your applications using voice and text.
- Amazon Comprehend: A service that makes it easy to extract insights from text.
- Amazon Translate: A service that makes it easy to translate text from one language to another.
- Amazon Personalize: A service that makes it easy to create personalized recommendations for your users.
- Amazon Forecast: A service that makes it easy to create forecasts for your time-series data.
- Amazon Fraud Detector: A service that makes it easy to detect fraud in your applications.
- Amazon Textract: A service that makes it easy to extract text and data from documents.
Google Cloud Platform(GCP)
Google Cloud is popular for its machine learning tools, including TensorFlow, which was developed by Google. It offers a range of cloud services that are accessible via an API. If you’re looking to build a machine learning model on your own, you can do so with one of its pre-trained models or start from scratch using one of its APIs. It also has a tool called AutoML that helps users build their own neural networks—if you don’t have any coding experience. You can also access other tools like BigQuery to query large datasets and Pub/Sub to publish messages to subscribers in real-time.
GCP is very easy to use, and its pricing structure is based on usage. It’s also easy to scale up or down as needed, which can be helpful if you don’t know how much traffic your site will get. You can also use it with other Google products like Gmail, Calendar, Drive, Docs, Sheets, Slides, and more.
Google has a free trial program that lets you test out its services before committing to a monthly payment plan.
Services provided by GCP for AI and ML
- Google Cloud AutoML: This service allows developers to train and deploy machine learning models with minimal coding. It also offers a variety of pre-trained models that can be used for a variety of tasks such as image classification, object detection, and text classification.
- Google Cloud BigQuery ML: This service allows developers to build machine learning models on top of BigQuery, Google's cloud data warehouse. Features include data pre-processing, model training, and model deployment.
- Google Cloud Datalab: This service provides an interactive development environment for data scientists and developers to work with data and build machine learning models.
- Google Cloud ML Engine: This service allows developers to train and deploy machine learning models on Google's cloud infrastructure. Hyperparameter tuning, model versioning, and autoscaling are some of its features.
- Google Cloud Natural Language: This service allows developers to process and analyze text data. It offers features such as entity recognition, sentiment analysis, and syntax analysis.
- Google Cloud Speech-to-Text: This service allows developers to convert speech to text.
- Google Cloud Text-to-Speech: This service allows developers to convert text to speech
- Google Cloud Translation: This service allows developers to translate text from one language to another. Features include support for multiple languages, automatic language detection, and the ability to create custom translation models.
- Google Cloud Vision: This service allows developers to analyze images. It offers a variety of features such as object detection, image classification, and optical character recognition.
- Google Cloud Video Intelligence: This service allows developers to analyze video data and features such as object detection, shot detection, and event detection.
Oracle Cloud
Oracle Cloud is popular for its ability to integrate with other cloud services, like Amazon Web Services. It also has a lot of support for machine learning. There are a number of pre-built machine learning models that can be accessed through APIs, or you can build your own custom model with TensorFlow or Spark ML. To take advantage of these features, you'll need an Oracle Cloud account, which costs $0.25 per hour for an on-demand instance and $0.01 per hour for an instance that runs only when needed (reserved instances). If you're looking to do some serious machine learning on a budget, you might want to check out Google Cloud instead—it offers free trial credits as well as plenty of ways to get started with its platform without spending any money at all. But if you are looking to make a significant investment in your data infrastructure, then Oracle Cloud could be worth exploring further.
Services provided by Oracle Cloud for AI and ML
- Oracle Cloud Machine Learning: Provides a managed environment for developing, training, and deploying machine learning models. It includes a variety of tools for data preparation, model development, and model deployment.
- Oracle Cloud Natural Language Processing: Provides APIs for natural language processing tasks such as text classification, entity extraction, and sentiment analysis.
- Oracle Cloud Predictive Analytics: This service Provides APIs for predictive analytics tasks such as regression, classification, and anomaly detection.
- Oracle Cloud Recommendations: Provides APIs for recommendation tasks such as product recommendations and content recommendations.
- Oracle Cloud Vision: Provides APIs for image recognition and object detection.
- Oracle Cloud Speech: This service provides APIs for speech recognition and text-to-speech.
- Oracle Cloud Video: Provides APIs for video analysis, such as facial recognition, object detection, and motion detection.
- Oracle Cloud Web Analytics: Provides APIs for web analytics, such as clickstream analysis and web page classification.
- Oracle Cloud Data Science: Provides a managed environment for developing, training, and deploying data science models. It includes a variety of tools for data preparation, model development, and model deployment.