What Is AI Cloud Computing & 11 Platforms to Supercharge It

Organizations strive to create and deploy AI models that achieve their business objectives. However, getting AI models from the lab into the real world is often challenging and can derail AI initiatives. AI cloud computing can simplify this process, enabling faster, smarter, and more efficient AI development that drives innovation and real-world impact. In this article, we'll explore how AI cloud computing can help you overcome the challenges of model machine learning deployment. AI inference APIs are among the most valuable tools for achieving your AI model deployment objectives. These AI cloud computing solutions enhance the performance of your AI models and simplify deployment, so you can get back to what matters: achieving your business goals.

What is AI Cloud Computing?

AI cloud computing combines artificial intelligence capabilities like machine learning, deep learning, and natural language processing with the scalability and flexibility of cloud infrastructure. Cloud providers enable organizations to access powerful AI tools without building or managing their own infrastructure.

Why Does AI Matter in Cloud Computing?

AI and cloud computing are ideal matches. An enormous amount of data is needed to train AI models, and the cloud provides access to vast datasets, making the two a perfect combination. The power needed to extract insights from large amounts of data (often found in serverless computing) can also be difficult to come by, but suddenly, it becomes easily accessible with the cloud. The meshing of AI technology with cloud environments creates possibilities for organizations of all sizes. Before the two came together, organizations without massive processing power could not leverage AI applications. Still, the cloud opens up those possibilities to SMBs and startups, empowering all organizations to leverage the potential of AI.

What are the Applications of AI in Cloud Computing?

Cloud computing and AI platforms are fundamentally changing the way we interact digitally. This includes how we connect on social channels, tap knowledge resources, assess risk in finance, shop online, get entertained, or manage healthcare, as well as the smart home devices and assistants we invite into our living rooms. The list is endless. Public cloud services eliminate the need for developers to build and manage infrastructure just to host AI platforms. Instead, they offer ready-made configurations and models to simplify testing and deploying AI applications. Though not necessarily requiring machine learning models, speech-to-text, analytics, and data visualization, generic AI services can be enhanced on the cloud using an organization’s first-party data. Some of the most common AI cloud computing applications in the cloud include:

• Intelligent Chatbots: Cloud-based chatbots leverage natural language processing to understand customer queries and respond appropriately, automating conversations.
• Recommendation Engines: These systems generate personalized product/content recommendations for each user by analyzing their preferences and purchasing data on the cloud.
• Predictive Maintenance: Industrial IoT sensor data is processed via cloud analytics and machine learning algorithms to forecast equipment issues before they occur.
• Fraud Detection: Cloud AI tracks patterns in online user behavior and transactions to automatically identify potential banking/payment fraud and suspicious account activity.
• Smart Inventory Management: AI in supply chain management applications combine inventory data, demand forecasts, and external signals to optimize stock levels and logistics in the cloud.
• Medical Image Analysis: Healthcare cloud platforms allow the scanning and testing of visual data with computer vision models rapidly for insights into conditions, diseases, etc.
• Automated Customer Support: Chat and voice bots accessing customer history data provide 24/7 automated technical support and resolve common user queries without human agents.
• Sentiment Analysis: NLP algorithms parse through textual data from reviews, surveys, and social media to categorize consumer sentiment and extract insights.
• GPU in Cybersecurity: Cloud-based AI systems detect potential malware, unauthorized access attempts, compromised accounts, and thwart threats.
• Large AI Model Training: Cloud ML Ops platforms support rapid development, iteration, testing, deployment, and ongoing improvement of machine learning models at scale.

What are the Pros and Cons of AI in Cloud Computing?

Using AI and cloud computing in tandem can provide many benefits for businesses, but there are also some issues we’ve yet to overcome. Some typical business applications for AI in cloud computing include chatbots, IoT, AI as a service, cognitive computing, business intelligence, predictive analytics, supply chain optimization, and process improvement. Employing all of this emerging technology in the cloud has multiple pros and cons. The pros of using AI in cloud computing include:

Cost Savings

Organizations see significant cost savings with AI and cloud computing. Cloud automations and AI applications are helping automate processes and reducing the need for manual work and additional staff, driving greater cost savings.

Scalability

Combining AI with cloud computing allows for growth and easy scaling. Imagine a startup that suddenly gains popularity and needs to fulfill an unexpected influx of orders. Cloud-based solutions allow for greater utilization of technology resources at the most cost-effective price point.

Flexibility

AI and the cloud create flexibility. Because cloud technology leverages advanced processing power, businesses can handle the highs and lows of resource utilization. This can help reduce latency and improve the customer experience.

Data Access and Analysis

The two technologies create greater data access and analysis for businesses that could not gain insight from large datasets. Better access and data analysis are creating more insights and helping to reduce waste.

Efficiency

Cloud-based services paired with AI make it easier to do more with less. The processing power provided by the cloud and the automation capabilities of AI make it easier to gain efficiencies in all-new areas of business.

Better Decision-Making

The vast amounts of data storage available in the cloud and the use of AI in trend recognition and predictive analytics enable better decision-making. The cons of using cloud technology and AI together include:

Connectivity Concerns

Cloud computing requires an internet connection. Unreliable connections can impact the functionality of AI applications. More reliable connectivity would be needed to leverage cloud computing technology for AI use. The solution to this challenge could be in edge computing, more specifically with a secure access server edge, allowing for speedy, secure connectivity on any device and from anywhere.

Undefined Business Objectives

Sometimes, organizations just want to be on the cutting edge, but technology for its own sake doesn’t help them progress toward their business goals. Knowing how they want to use technology and mapping their business objectives directly to the technology they plan to implement is essential.

Data Privacy

Organizations often use vast data, including personal details, to train AI. This can make AI and the cloud a target for threat actors. Securing that data is challenging, and data protection is usually a risk.

Related Reading

• AI Workloads
• CPU Acceleration

Top 11 AI Cloud Platforms

1. Inference: The OpenAI-Compatible Serverless Inference API

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. DataRobot: The Enterprise AI Platform to Solve Business Problems

DataRobot is a cutting-edge enterprise AI platform that revolutionises how businesses access and implement ML. Its comprehensive suite of tools automates the entire lifecycle of ML models, from initial construction to deployment and ongoing management. DataRobot doesn't just provide technology; it offers extensive AI implementation services, training programmes, and support to ensure clients can fully leverage the power of AI.

The company's flagship offering, AI Cloud for Industries, is a tailored solution that addresses the unique AI challenges and opportunities across various sectors. This industry-specific approach allows organisations to harness AI's potential in a way that's directly relevant to their field, fostering innovation and competitive advantage in today's rapidly evolving digital landscape.

3. Amazon SageMaker: The Fully Managed ML Service by AWS

Amazon SageMaker, a fully managed ML service by Amazon Web Services (AWS), is ranked as one of the top AI tools for 2024.

• It simplifies the entire ML workflow, from data preparation and model training to deployment and monitoring.
• Amazon SageMaker offers a scalable and cost-effective solution for enterprises to build and deploy AI applications.
• Amazon SageMaker provides a seamless workflow for building, training, and deploying machine learning models, streamlining development.
• It offers tools for model optimisation, enabling efficient use of resources and reducing inference costs.

SageMaker allows teams to collaborate, share, and experiment with models, fostering innovation and productivity. It also helps predict maintenance needs for machinery and equipment, optimise maintenance schedules, and reduce downtime. It can be used to build recommendation engines for personalised customer experiences in various domains, such as e-commerce and entertainment.

4. Google AI Cloud Platform: The Suite of Cloud-Based Tools and Services to Harness AI

Google AI Cloud Platform Google AI Cloud Platform is a cutting-edge suite of cloud-based tools and services that empowers businesses and developers to harness the power of AI and ML for a wide range of applications. With a robust infrastructure and a wealth of AI-driven capabilities, the Google AI cloud platform has become a go-to solution for organisations looking to drive innovation and stay competitive in today's digital landscape.

Google AI cloud platform offers a comprehensive set of AI building blocks, including:

• Pre-trained models
• APIs
• Tools for vision, language, and structured data

This feature allows organisations to create custom ML models without requiring extensive expertise. AutoML simplifies the model training process, making it accessible to a broader audience and accelerating the development of AI-driven solutions. Google AI cloud platform provides robust data storage and processing capabilities. It integrates seamlessly with BigQuery, Google's fully managed data warehouse, enabling organisations to analyse massive datasets and derive valuable insights.

5. Microsoft Azure AI: The Cloud Platform for Enterprise AI Solutions

Microsoft Azure AI offers comprehensive AI tools and services for various business needs. It enables building, training, and deploying AI models at scale and integrates seamlessly with other Microsoft services.

Azure AI provides pre-built models for vision, language, and speech recognition, saving development time. It adheres to robust security and compliance standards. Azure AI powers virtual agents for customer service, assists in anomaly detection for fraud and cybersecurity, and enables personalised marketing campaigns through customer behaviour analysis.

6. Salesforce Einstein Cloud: The AI Cloud Built for Customer Relationship Management

Salesforce Einstein AI Cloud enhances customer relationship management and drives intelligent decision-making. Using predictive analytics, it optimizes sales, marketing, and customer service strategies. The platform enables personalised customer interactions and automates routine tasks, improving efficiency.

Einstein AI Cloud helps sales teams identify promising leads, predict outcomes, and recommend actions. It assists customer service teams in providing efficient, personalised support by analysing interactions and feedback, improving customer satisfaction.

7. IBM Watson: The AI Cloud with Cognitive Computing Capabilities

IBM Watson is a leading AI cloud platform with cognitive computing capabilities. It can understand, reason, and learn from data and interactions. Watson integrates with existing systems and is customisable for specific business needs.

It's used across various industries, including:

• Healthcare
• Finance
• Retail

Watson assists in medical diagnosis, drug discovery, and personalised patient care. It powers virtual assistants for customer service and aids in financial risk assessment and fraud detection.

8. Oracle Cloud AI: The AI Cloud Suite for Operational Efficiency

Oracle Cloud AI is a suite of AI services designed to drive innovation and improve operational efficiency across industries. It offers AI and ML services including image recognition, NLP, predictive analytics, and recommendation engines.

Oracle's Autonomous Database uses AI to automate management tasks, enhancing security and performance. The platform enables the development of intelligent chatbots and virtual assistants for customer service. Industries like manufacturing can use it for predictive maintenance, while marketers can create personalised campaigns, improving customer engagement and conversion rates.

9. Alibaba Cloud AI: The Comprehensive AI Cloud Platform

Alibaba Cloud AI offers comprehensive AI and ML services. It provides pre-built AI algorithms and models for tasks such as NLP, image recognition, and speech recognition. The platform includes tools for building and training custom ML models, empowering organisations to develop tailored AI solutions.

Alibaba Cloud AI facilitates advanced data analytics and business intelligence through AI-driven processing. It enhances customer experiences in eCommerce and retail with personalised recommendations and visual product searches. It assists with medical image analysis, disease diagnosis, and drug discovery in healthcare.

10. Huawei Cloud AI: The High-Performance Computing AI Platform

Huawei Cloud AI is an AI and ML platform by Huawei Technologies. It provides services for training and deploying custom AI models and offers high-performance computing resources, including GPU instances. Users can access pre-built AI models and algorithms for quicker development.

Huawei Cloud AI includes Natural Language Processing capabilities for language translation and chatbot development tasks. It can be used to create innovative city solutions, enhancing public safety and traffic management. It assists with medical image analysis, disease diagnosis, and predictive analytics in healthcare.

11. Wipro Holmes: The AI Cloud Platform for Cognitive Computing and Automation

Wipro Holmes stands at the forefront of AI and automation technology, offering a sophisticated platform that seamlessly integrates cognitive computing, hyper-automation, robotics, cloud technologies, and advanced analytics. This powerful combination enables businesses to adapt to and thrive in the digital revolution. The platform's comprehensive approach covers every aspect of AI deployment, from initial development to publication, metering, governance, and monetisation.

Wipro Holmes excels in providing cutting-edge solutions such as digital virtual agents and process automation, while also extending its capabilities to support emerging technologies like robotics and drones. This holistic ecosystem empowers organisations to navigate the complexities of digital transformation, driving efficiency, innovation, and growth across their operations.

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• GPU vs. CPU for AI
• Edge Inference

AI Adoption Challenges in Cloud Environments

AI and cloud computing have great potential as partners, but the union has a fair share of challenges. Data security is one of the most pressing issues for organizations to address when implementing AI in cloud settings.

Cloud-based AI relies heavily on data storage and processing, raising concerns about data security breaches and privacy violations. Ensuring robust encryption, access controls, and compliance with regulations like GDPR is crucial to safeguarding sensitive information.

Latency and Bandwidth Limitations: Speeding Up Data Transfer

Another hurdle organizations must overcome when deploying AI in cloud settings: latency and bandwidth limitations. Transferring large volumes of data between the cloud and devices can introduce latency issues, impacting real-time AI applications like autonomous vehicles or IoT devices. Optimizing data transfer protocols can help mitigate these challenges.

Scalability and Resource Management: Balancing Computational Needs

AI can require substantial computing resources. Organizations deploying AI in the cloud must balance these fluctuating needs to avoid performance issues. By leveraging Hyperstack Cloud's scalable infrastructure, you can adapt to changing needs and maximize the efficiency of your AI deployments.

Cost Management: Keeping AI Deployment Affordable

Cloud-based AI deployments can incur unpredictable costs, challenging budgeting and cost management. Hyperstack Cloud offers transparent cloud GPU pricing, billed per minute, allowing you to monitor and control AI deployment costs effectively. Our competitive pricing model ensures that you only pay for what you use instead of the GPU.

Cloud Delivery Models: Choosing the Right Approach for Your Business

Many organizations are moving toward hybrid cloud or multi-cloud environments. The cloud has made so many types of as-a-service offerings available to businesses that you can outsource nearly any support. However, most offerings are a subcategory of the following three models:

SaaS

Software-as-a-service includes any cloud-based tools your business uses that don’t reside on on-premise servers. Popular choices include:

• Salesforce
• Hubspot
• Slack

PaaS

Platform-as-a-service is a cloud environment with operating systems and all the tools needed to develop applications without specialized coding or complex development. Some of the most-used PaaS tools include:

• IBM Cloud
• AWS Elastic Beanstalk
• Microsoft Azure

IaaS

Infrastructure-as-a-service provides the underlying technology architecture so organizations can forego the cost of servers, networking equipment, and physical data centers. Common IaaS tools include:

• Google Cloud
• Amazon EC2
• IBM

The primary challenge with implementing AI regarding these various delivery models involves data integration. Ensuring data is available across a multi-cloud or hybrid setup can be complex and a hindrance to properly deploying AI. You’ll need a precise flow of data from one delivery model to another so that AI can harness the insights within your data. This is often cumbersome and frequently involves overwhelming complexity to achieve.

Cloud Technologies: Understanding the Tools for AI Deployments

Some cloud technologies have limitations when managing AI and machine learning workloads. Containers work on the cloud by segmenting everything needed for an application to run into a single package. Kubernetes, known as K8, is one of the most widely used cloud technologies.

Kubernetes is an open-source platform for managing containers. Although it offers many benefits, it is a highly complex tool requiring extensive troubleshooting knowledge. Additionally, if your system hasn’t been designed with a cloud-native mindset, the underlying layers may not operate correctly, making your AI applications obsolete.

Skills Gaps: Finding Qualified Professionals

Deploying AI in the cloud requires multiple skill sets to merge all the different functionalities. Organizations need skill sets in cloud, data science, and security to operate as expected, with specialized knowledge required for various platforms and tools.

CompTIA’s State of the Tech Workforce 2023 reports that data scientists and analysts are the top growth occupations. Cloud security is also becoming a top priority as organizations seek to protect customer data and meet compliance regulations. The talent pipeline isn’t developing skills quickly enough to meet these areas' high job demand.

Related Reading

• Pros and Cons of Serverless Architecture
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Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.