Artificial Intelligence as a Service (AlaaS) helps organizations incorporate artificial intelligence (AI) functionality without the associated expertise. Usually, AIaaS services are built upon cloud-based providers like Amazon AWS, Google Cloud, Microsoft Azure, and IMB Cloud, used as IaaS. The AI service, framework, and workflows built upon these infrastructures are offered to final customers for various use cases (e.g., inventory management services, manufacturing optmizations, text generation).
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Understanding Artificial Intelligence as a Service
Artificial Intelligence as a Service allows businesses to experiment with artificial intelligence in a low risk environment and without a significant upfront investment.
AlaaS is a more recent addition to a suite of “as a service” products that help businesses maintain a focus on their core operations. It is becoming increasingly popular, with the International Data Corporation predicting that 75% of commercial enterprise applications will use AI in the coming years. As a result, large organizations such as Amazon, Google, IBM, and Microsoft all now offer AlaaS to customers.
To understand this industry, it’s important to understand its various layers. Just like SaaS, built on top of IaaS, PaaS, also AIaaS, is built on top of cloud infrastructure that works as the basis for the service itself.
The “as-a-service” models are typical of the second wave of Web 2.0, built on top of cloud computing. Indeed, these models’ basic premise is to offer a solution to the final customer without having to host it on-premise, with complex implementations and large overhead. Yet while PaaS and IaaS are skewed toward development teams. SaaS has wider applications toward end-users, also in non-technical departments.
The various types of AlaaS
AlaaS is a relatively broad term that can be divided into distinct types:
Cognitive computing APIs – where an application programming interface (API) developer can utilise API calls to incorporate artificial intelligence into applications. This encompasses a range of services including computer vision, knowledge mapping, and natural language processing (NLP). Each has the ability to generate businessvalue from unstructured information.
Bots and digital assistance – a very popular form of AlaaS including automated email services, chatbots, and digital customer service agents.
Fully-managed machine learning services – ideally suited to non-technological organizations who desire a fully managed approach. These services invariably offer customer templates and pre-built models. For the most technologically-challenged, they also offer code-free interfaces.
Machine learning frameworks – or frameworks that allow organizations to build custom models that will only handle a small amount of data.
Advantages of Artificial Intelligence as a Service
In an increasingly automated digital world, there are a multitude of benefits to AlaaS.
Here are just a few of them:
Reduced cost. AlaaS helps small and medium-sized companies, in particular, become more profitable by minimizing outlay. Profitability increases as companies are able to avoid hiring programmers or investing in expensive machinery. Put differently, they don’t need to build, test, and implement artificial intelligence systems from scratch.
Ease of use. The vast majority of AlaaS companies offer packaged products that don’t require expertise to implement. Having said that, developers from the business using AlaaS can easily tweak the product if desired.
Scalability and flexibility. Some businesses will be uncertain as to whether Artificial Intelligence as a Service is right for them. This uncertainty can be alleviated by starting small and then scaling later as knowledge and confidence increase or corporate requirements change. To help facilitate the onboarding of AlaaS, many providers offer their services at a fixed rate. This increases flexibility because customers are free to pay for what they use, and no more.
Ecosystem growth and integration. The most robust systems are fully integrated, but integration is hindered when artificial intelligence can only be used in a small subset of business operations. Companies such as NVIDIA and Siemens have partnered with AlaaS vendors to overcome incompatible technologies – thereby allowing product teams to increase integration, speed, and efficiency.
How Does AIaaS get monetized?
As the world itself explains, AIaaS gets monetized in the form of subscription/retainer that comprises the management, running and monitoring of the AI/ML Models that are used as the foundation fo the provided service.
Imagine the specific case of a company providing AI models for improving manufacturing processes. The AIaaS company will work on cleaning the data from the customer, plugging that into its AI models to generate reports, monitoring, and workflows for process optimization.
Imagine also the case of a company providing NLG (natural language generation or automatic text generations using the latest language models), which will be running, and operating those models while the customer gets as output generated pages or workflows, paid in the form of retainer.
Part of AI services will also require maintenance, or new experimental projects can be undertaken. In these cases, those can be part of the retainer or charged separately on a pay per consumption basis as MLOps.
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.
Key takeaways
Artificial Intelligence as a Service allows businesses to incorporate AI functionality without the requisite knowledge or experience.
Artificial Intelligence as a Service can be divided into four distinct categories: cognitive computing APIs, bots and digital assistance, machine learning frameworks, and fully managed machine learning services.
Artificial Intelligence as a Service offers a number of benefits to customers. AlaaS is a flexible and scalable service that reduces operating costs and is relatively simple to use. As more organizations work toward full integration, the service itself will become more efficient.
Key Highlights
Introduction to AIaaS:
AIaaS enables organizations to integrate AI functionality without requiring in-depth AI expertise.
Cloud-based providers like Amazon AWS, Google Cloud, Microsoft Azure, and IBM Cloud serve as the foundation for AIaaS offerings.
Benefits and Use Cases:
AIaaS allows businesses to experiment with AI in a low-risk environment without significant upfront investments.
It falls under the “as-a-service” suite of products, similar to SaaS, PaaS, and IaaS.
Offers various use cases, including inventory management, manufacturing optimization, and text generation.
Layers of AIaaS:
AIaaS is built on top of cloud infrastructure, similar to how SaaS and PaaS are built on IaaS.
The “as-a-service” models offer solutions to customers without complex on-premise implementations.
Types of AIaaS:
Cognitive computing APIs allow developers to incorporate AI through API calls for tasks like computer vision and natural language processing.
Bots and digital assistance, such as chatbots, enhance customer service and automated interactions.
Fully-managed machine learning services provide templates and pre-built models, suitable for non-technical organizations.
Machine learning frameworks allow building custom models for specific tasks.
Advantages of AIaaS:
Reduced costs, especially for small and medium-sized companies, by avoiding the need for extensive AI expertise and infrastructure.
Ease of use with packaged products that can be easily implemented and customized.
Scalability and flexibility, enabling businesses to start small and expand as needed.
Ecosystem growth and integration, allowing AI to be seamlessly integrated into various business operations.
Monetization of AIaaS:
AIaaS is monetized through subscription/retainer models, encompassing management, operation, and monitoring of AI/ML models.
AIaaS companies clean and process customer data to generate reports, monitoring, and optimization workflows.
Maintenance, new projects, and MLOps (Machine Learning Ops) may be part of the subscription or charged separately.
MLOps:
MLOps includes best practices, workflows, and processes to create, run, and maintain machine learning models for operational processes.
Key Takeaways:
AIaaS enables businesses to incorporate AI without extensive expertise.
It encompasses various categories like cognitive computing APIs, bots, machine learning frameworks, and managed services.
Benefits include cost reduction, ease of use, scalability, flexibility, and ecosystem integration.
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results.
Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual.
Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
Agile started as a lightweight development method compared to heavyweight software development, which is the core paradigm of the previous decades of software development. By 2001 the Manifesto for Agile Software Development was born as a set of principles that defined the new paradigm for software development as a continuous iteration. This would also influence the way of doing business.
Agile project management (APM) is a strategy that breaks large projects into smaller, more manageable tasks. In the APM methodology, each project is completed in small sections – often referred to as iterations. Each iteration is completed according to its project life cycle, beginning with the initial design and progressing to testing and then quality assurance.
Agile Modeling (AM) is a methodology for modeling and documenting software-based systems. Agile Modeling is critical to the rapid and continuous delivery of software. It is a collection of values, principles, and practices that guide effective, lightweight software modeling.
Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.
Business modelinnovation is about increasing the success of an organization with existing products and technologies by crafting a compelling value proposition able to propel a new business model to scale up customers and create a lasting competitive advantage. And it all starts by mastering the key customers.
That is a process that requires a continuous feedback loop to develop a valuable product and build a viable businessmodel. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problem and not the technical solution of its founders.
A design sprint is a proven five-day process where critical business questions are answered through speedy design and prototyping, focusing on the end-user. A design sprint starts with a weekly challenge that should finish with a prototype, test at the end, and therefore a lesson learned to be iterated.
Tim Brown, Executive Chair of IDEO, defined design thinking as “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.” Therefore, desirability, feasibility, and viability are balanced to solve critical problems.
DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.
Product discovery is a critical part of agile methodologies, as its aim is to ensure that products customers love are built. Product discovery involves learning through a raft of methods, including design thinking, lean start-up, and A/B testing to name a few. Dual Track Agile is an agile methodology containing two separate tracks: the “discovery” track and the “delivery” track.
Feature-Driven Development is a pragmatic software process that is client and architecture-centric. Feature-Driven Development (FDD) is an agile software development model that organizes workflow according to which features need to be developed next.
eXtreme Programming was developed in the late 1990s by Ken Beck, Ron Jeffries, and Ward Cunningham. During this time, the trio was working on the Chrysler Comprehensive Compensation System (C3) to help manage the company payroll system. eXtreme Programming (XP) is a software development methodology. It is designed to improve software quality and the ability of software to adapt to changing customer needs.
The Agile methodology has been primarily thought of for software development (and other business disciplines have also adopted it). Lean thinking is a process improvement technique where teams prioritize the value streams to improve it continuously. Both methodologies look at the customer as the key driver to improvement and waste reduction. Both methodologies look at improvement as something continuous.
A startup company is a high-tech business that tries to build a scalable business model in tech-driven industries. A startup company usually follows a lean methodology, where continuous innovation, driven by built-in viral loops is the rule. Thus, driving growth and building network effects as a consequence of this strategy.
Kanban is a lean manufacturing framework first developed by Toyota in the late 1940s. The Kanban framework is a means of visualizing work as it moves through identifying potential bottlenecks. It does that through a process called just-in-time (JIT) manufacturing to optimize engineering processes, speed up manufacturing products, and improve the go-to-market strategy.
RAD was first introduced by author and consultant James Martin in 1991. Martin recognized and then took advantage of the endless malleability of software in designing development models. Rapid Application Development (RAD) is a methodology focusing on delivering rapidly through continuous feedback and frequent iterations.
Scaled Agile Lean Development (ScALeD) helps businesses discover a balanced approach to agile transition and scaling questions. The ScALed approach helps businesses successfully respond to change. Inspired by a combination of lean and agile values, ScALed is practitioner-based and can be completed through various agile frameworks and practices.
The Spotify Model is an autonomous approach to scaling agile, focusing on culture communication, accountability, and quality. The Spotify model was first recognized in 2012 after Henrik Kniberg, and Anders Ivarsson released a white paper detailing how streaming company Spotify approached agility. Therefore, the Spotify model represents an evolution of agile.
As the name suggests, TDD is a test-driven technique for delivering high-quality software rapidly and sustainably. It is an iterative approach based on the idea that a failing test should be written before any code for a feature or function is written. Test-Driven Development (TDD) is an approach to software development that relies on very short development cycles.
Timeboxing is a simple yet powerful time-management technique for improving productivity. Timeboxing describes the process of proactively scheduling a block of time to spend on a task in the future. It was first described by author James Martin in a book about agile software development.
Scrum is a methodology co-created by Ken Schwaber and Jeff Sutherland for effective team collaboration on complex products. Scrum was primarily thought for software development projects to deliver new software capability every 2-4 weeks. It is a sub-group of agile also used in project management to improve startups’ productivity.
Scrum anti-patterns describe any attractive, easy-to-implement solution that ultimately makes a problem worse. Therefore, these are the practice not to follow to prevent issues from emerging. Some classic examples of scrum anti-patterns comprise absent product owners, pre-assigned tickets (making individuals work in isolation), and discounting retrospectives (where review meetings are not useful to really make improvements).
Scrum at Scale (Scrum@Scale) is a framework that Scrum teams use to address complex problems and deliver high-value products. Scrum at Scale was created through a joint venture between the Scrum Alliance and Scrum Inc. The joint venture was overseen by Jeff Sutherland, a co-creator of Scrum and one of the principal authors of the Agile Manifesto.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
