Artificial Intelligence is advancing daily, and it will be used to power customer experiences and business process optimization in the future. Given the wealth of data being unlocked by the AI revolution, business executives undoubtedly want to use AI to their advantage. The good news is that higher efficiencies can be achieved at a reasonable cost with artificial intelligence-as-a-service (AIaaS), which combines AI services with the SaaS business model. AIaaS can assist app developers in maximizing the potential of cloud-based solutions. Are you prepared to use AIaaS to drive forward? We’ve compiled important aspects into this enlightening What is AIaaS guide.
What is AIaaS?
What is AIaaS, A range of AI tools (typically APIs) are referred to as Artificial Intelligence as a Service (AIaaS). Here, off-the-shelf solutions are provided by third-party vendors who offer these tools. AIaaS enables businesses to adopt and use AI solutions at a lower risk and with little financial outlay. Platforms for AI providers now provide a variety of machine learning (ML) and AI approaches. The suppliers offer a range of AI solutions to meet the needs of an organization. Thus, businesses must compare features and costs to determine which option is best for them.
Additionally, AI cloud services such as Google Cloud Machine Learning, Microsoft Cognitive Services, and Amazon Machine Learning assist businesses in finding potential uses for their data. Companies have the fantastic chance to test out the algorithms and services offered by different providers. This enables them to assess and determine what works best for their business, enabling them to decide whether to scale.
Different Types of AIaaS
- Bots: It takes a lot of work on the part of developers to make chatbots successful. A conversational interface can go wrong very quickly, but an interesting chatbot using AI algorithms can mimic human communication. Understanding user inquiries and providing essential answers can be facilitated by a careful blending of NLP and ML capabilities. Bots are revolutionizing customer service today. They increase customer satisfaction and lower first-time response rates. Additionally, routine tasks can be avoided with automation, freeing up agents’ time so they can concentrate on more difficult tasks.
InPost, the parcel delivery company with the fastest rate of growth in Europe, recently revealed that they use chatbots to automate up to 92% of the millions of customer conversations they handle annually. Another example is Microsoft’s XiaoIce, one of the state-of-the-art AI chatbots and another success story (alongside GPT-3). Furthermore, because of its market presence, its valuation approached $1 billion in 2021.
- Application Programming Interface (API): AIaaS solutions provide interesting application program interfaces (APIs), which allow services to talk to one another. APIs function as a bridge, enabling communication between two programs. Sentiment analysis is now possible thanks to natural language processing APIs. Among other things, they are also capable of extracting entities from text. Developers only need to write a few lines of code to adopt and implement APIs when they are provided “as a service.” Among the choices for APIs are:
- NLP
- Search
- Translation
- Emotion detection
- Knowledge mapping
- Computer Speech and computer vision
- Machine Learning: Developers frequently utilize popular tools like AI and ML frameworks to create models. Additionally, make predictions, find patterns in vast amounts of data, and optimize procedures. Although machine learning (ML) is often linked to big data, these frameworks enable the development of machine learning tasks independently of big data environments. Businesses will find it simple to implement ML technology with AIaaS. To meet their unique business needs, one can use tools that have been specially designed or pre-trained models. And without any prior knowledge of machine learning.
Benefits of AIaaS
- Cost Reduction: The fact that AI as a Service lowers costs—including those associated with developing AI solutions—is one of its main benefits. Additionally, since AIaaS allows for pay-per-use pricing, businesses can take advantage of price transparency and only pay for what they need.
- Speed: AIaaS helps save time spent developing AI solutions with the added benefit of cost reduction. AI projects move more quickly thanks to the creative approach.
- High-tech Infrastructure Available: The powerful and quick GPUs required to implement AI and ML models are now more easily accessible thanks to AIaaS. It is beneficial to have access to high-tech infrastructure, particularly since the majority of SMEs lack the time and resources to develop solutions internally. Additionally, because AIaaS is customizable, companies can create a unique task-oriented model.
- Scalability: For companies looking to grow, AIaaS is a great choice. It works well for jobs that don’t add much value to the task itself but do call for some degree of cognitive judgment.
- Customization: We’ve never heard of companies with the same objectives! You’re right, objectives are always unique to a business. Thus, AIaaS can be adjusted to match the needs of the business, data, or project by using different natural goals.
Notable AIaaS Examples
- IBM Watson: With its AIaaS products, IBM has a tried-and-true strategy. Additionally, IBM Watson offers a range of AI tools that support businesses in gaining a decisive competitive advantage. Companies can select from a variety of pre-built applications, including Watson Natural Language Understand, which can handle complex text analysis tasks, and Watson Assistant, which can be used to create virtual assistants. Furthermore, no prior knowledge of data science or machine learning is required. Additionally, developers can create, train, and implement machine learning models on any cloud by using IBM Watson Studio.
- Microsoft Azure: Microsoft’s public cloud computing platform Azure AI provides yet another fascinating example. For developers, the platform provides a wealth of AI and ML solutions. It would be wonderful for developers to investigate Azure Cognitive Services to learn about various AI features (like text extraction or computer vision) and incorporate them into their apps through APIs. Additionally, you can investigate the Azure Bot Service, which lets you easily construct any kind of bot—from a Q&A bot to a virtual assistant with your logo.
- Google Cloud ML: Companies can keep an eye out for Google Cloud ML Engine, the company’s AI platform, for cutting-edge ML goods and services. Companies can develop and implement machine learning projects with the aid of this platform. The custom machine-learning models that AutoML can train for text analysis, image classification, translation, and other uses can be explored by developers at their leisure. With the use of metrics to evaluate performance and a “what-if tool,” it makes it simple to visualize the datasets and see how your model performs.
Challenges of AIaaS
- Data Governance: Businesses in highly regulated sectors are required to restrict cloud data storage. Businesses in the banking and healthcare sectors might not be able to fully utilize AIaaS.
- Efforts For Bug-free Implementation: Implementing the potentially flawed AIaaS software is still another issue. Additionally, a smooth and successful transition necessitates a great deal of work in the implementation.
- Vendor Lock-in: Assume you are utilizing an alternative API that has distinct response formats. Though switching between the different response formats and APIs may seem simple, it takes work. Moreover, because the development team must become acquainted with end-to-end ML services or even ML components, it is more difficult to switch tools. Because all of these factors contribute to vendor lock-in, businesses must comprehend how difficult it is to move between competing products.
- Available Budget: What is your current budget available? Is there a good chance that in the upcoming months or years, your budget will go up or down? Does your monthly available cash flow fluctuate or is it consistent? You can ascertain whether you can currently and in the future afford your preferred solutions by posing these questions.
- In-house AI Skills and Development Expertise: Is there a significant portion of your in-house AI expert knowledge lacking, or do you have a large and highly skilled tech team that has experience with AI and related development projects already? Determining whether you require an AIaaS that manages the entire AI lifecycle or one that offers customers more customization and self-service options can be aided by evaluating the skills of your team and proactively defining their AI roles and responsibilities.
- Compliance and Security Requirements: Do you work in a field or in an area where data security and other compliance regulations are strictly enforced? Do you routinely handle personally identifiable information (PII) or other data that is particularly susceptible to cyberattacks? If so, you should look for an AIaaS provider that is compatible with your cybersecurity and compliance management frameworks, or at the very least, one that offers built-in or simple-to-integrate security and compliance features.
Conclusion
What is AIaaS, Businesses are using AIaaS to drive innovation, efficiency, and competitiveness. This is a paradigm shift in how companies use AI. Organizations may seize new opportunities, improve customer value, and streamline processes by adopting AIaaS. AIaaS will surely be crucial in determining how business will develop in the future across all industries as AI develops, opening the door for previously unheard-of expansion and change.
