How the top 4 cloud vendors are applying AI to cloud computing

Cloud computing and Artificial Intelligence (AI) have been complementary factors to the growth of digital transformation because one cannot exist without the other in the modern tech landscape. With the recent rise of generative AI platforms like ChatGPT (an AI chatbot that uses the Microsoft Azure cloud), 2024 is the year of AI. 

In this article, we explore the contributions of popular platforms that harness the power of AI and cloud computing and are massive players in the AI revolution.

Google Cloud Vertex AI

Vertex AI is the enterprise cloud platform provided by Google Cloud, which offers a robust set of tools and services for machine learning operations. Vertex AI is particularly of interest to data scientists and developers for custom AI app development, smart search, and conversational intelligence. Here are a few key applications:

• Tactical AI/ML tools: Vertex AI is an organized set of AI-based building tools. It comprises pre-trained models, APIs, and tools for computer vision, NLP, and structured data analytics. Vertex AI also practices advanced ML operations tools (MlOps) to assist with model development, management, and deployment.
• AutoML: This unique aspect of Vertex AI allows developers to engineer machine learning models with the least amount of coding. This assists in the rapid deployment of AI-based solutions that are accessible to a large user base.
• Data analytics: Vertex AI facilitates extensive data storage and processing capabilities. It is compatible with BigQuery, which is Google’s automated data warehouse. This feature helps with the data analysis of huge datasets to gain critical insights.
• Model Garden: Model Garden is an ML library in Vertex AI that enables users to discover, test, customize, and deploy models and assets. This can be applied to Google’s proprietary data models like Gemini and other open-source models like TensorFlow.

Amazon Web Services (AWS) AI

AWS is one of the global leaders in Amazon’s reliable cloud computing services. The AWS AI services help are a collection of ML-based technologies developers and engineers can use to create AI software. Some top applications include:

• Amazon Sagemaker is an AWS service useful for developing, training, and deploying machine learning models. The entire machine learning workflow is optimized for inference on cloud instances and edge devices, improving speed and reliability.
• Amazon Rekognition is an automated AI/ML service that uses advanced computer vision to provide image and video analysis, including face detection, liveness check, celebrity recognition, and more.
• Amazon Comprehend is a unique NLP service provided by AWS in text analysis. It can establish text-based relationships for data analytics using technologies like sentiment analysis and entity recognition.
• Amazon Lex is used to develop, test, and deploy chatbots and conversational interfaces using advanced NLP algorithms.

Microsoft Azure AI

Backed by the public cloud services provider Microsoft Azure, the Azure AI is a comprehensive collection of AI services and tools developers and data scientists use to develop, deploy, and monitor AI applications and solutions. The Azure AI infrastructure can host diverse AI scenarios, including ML, NLP, computer vision, etc. Some major applications include: 

• Azure Synapse Analytics (ASA): This cloud service by AWS harnesses big data and data warehousing to provide various data integration and processing abilities. Developers can use different programming languages, such as SQL, Python, and .NET, to develop data pipelines that can ingest, process, and monitor large datasets. 
• Azure Cognitive Services: These are a collection of pre-built APIs for vision, speech, language, and decision-making, including NLP, image and video analysis, and speech recognition. These APIs can be used to develop smart applications that process human interactions naturally. 
• Azure Bot Service: It is an AWS cloud service for developers to harness tools for building intelligent chatbots and other AI conversational systems. It can establish diverse resources for creating chatbots, including templates and pre-built connectors to well-known messenger platforms such as Skype, Slack, and Facebook Messenger. 

IBM Watsonx

IBM Watsonx is a comprehensive platform designed to accelerate the deployment of AI in enterprises by providing robust tools and services that enhance the entire AI lifecycle. Watsonx comprises three primary products: watsonx.ai, watsonx.data, and watsonx.governance. Each product brings unique capabilities that cater to various aspects of AI development and deployment, emphasizing scalability, governance, and seamless integration across multi-cloud environments. 

• Watsonx.ai: Watsonx.ai is a versatile AI studio that offers tools for building, tuning, and deploying AI models. It leverages both IBM’s proprietary models and open-source models from Hugging Face. It supports various foundation models, including IBM’s Granite and third-party models like Llama 3 and Mixtral 8x7b.
• Watsonx.data: It is a data store built on an open data lake-house architecture, optimized for AI and governed data workloads. It establishes a common platform for utilizing and monitoring data concerning on-premise and multi-cloud environments. It also empowers non-technical users to interact with enterprise data through a user-friendly interface, promoting collaboration and accelerating AI project timelines​.
• Watsonx.governance:  It offers comprehensive tools for managing AI lifecycle governance, ensuring transparency, fairness, and regulation compliance. It is based on standards like the EU AI Act to ensure appropriate monitoring and governance​.

Conclusion

2024 marks the peak of the AI revolution, and this article highlights the major players involved. These platforms establish the essential tools and infrastructure to develop innovative AI solutions across various industries.  Each platform offers unique features and technical capabilities, contributing significantly to advancements in artificial intelligence. From scalable infrastructure and pre-built models to automated machine learning and advanced analytics, these cloud services are essential for driving the future of AI.