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From self-driving cars to intuitive chatbots like OpenAI’s ChatGPT, artificial intelligence (AI) has made significant progress in the past decade.
Enterprises are looking to implement a broad spectrum of AI applications, from text analysis software to more complex predictive analytics tools. But building an in-house AI solution makes sense only for some businesses, as it’s a long and complex process.
With emerging data science use cases, organizations now require continuous AI experimentation and test machine learning algorithms on several cloud platforms simultaneously. Processing data through such methods need massive upfront costs, which is why businesses are now turning toward AIaaS (AI-as-a-service), third-party solutions that provide ready-to-use platforms.
The platform for modern analytics
AIaaS is becoming an ideal option for anyone who wants access to AI without needing to establish an ultra-expensive infrastructure for themselves. With such a cost-effective solution available for anyone, it’s no surprise that AIaaS is starting to become a standard in most industries. An analysis by Research and Markets estimated that the global market for AIaaS is expected to grow by around $11.6 billion by 2024.
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AIaaS allows companies to access AI software from a third-party vendor rather than hiring a team of experts to develop it in-house. This allows companies to get the benefits of AI and data analytics with a smaller initial investment, and they can also customize the software to meet their specific needs. AIaaS is similar to other “as-a-service” offerings like infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS), which are all hosted by third-party vendors.
In addition, AIaaS models enclose disparate technologies, including natural language processing (NLP), computer vision, machine learning and robotics; you can pay for the services you require and upgrade to higher plans when your data and business scale.
AIaaS is an optimal solution for smaller and mid-sized companies to access AI capabilities without building and implementing their own systems from scratch. This allows these companies to focus on their core business and still benefit from AI’s value, without becoming experts in data and machine learning. Using AIaaS can help companies increase profits while reducing the risk of investment in AI. In the past, companies often had to make significant financial investments in AI in order to see a return on their investment.
Moses Guttmann, CEO and cofounder of ClearML, says that AIaaS allows companies to focus their data science teams on the unique challenges to their product, use case, customers and other essential requirements.
“Essentially, using AIaaS can take away all the off-the-shelf problem-solving AI can help with, allowing the data science teams to concentrate on the unique and custom scenarios and data that can make an impact on the business of the company,” Guttmann told VentureBeat.
Guttmann said that the crux of AI services is essentially outsourcing talent, i.e., having an external vendor build the internal company’s AI infrastructure and customize it to their needs.
“The problem is always maintenance, where the know-how is still held by the AI service provider and rarely leaks into the company itself,” he said. “AIaaS on the contrary, provides a service platform, with simple APIs and access workflows, that allows companies to quickly adapt off-the-shelf working models and quickly integrate them into the company’s business logic and products.”
Guttmann says that AIaaS can be great for tech organizations either having pretrained models or real-time data use cases, enhancing legacy data science architectures.
“I believe that the real value in ML for a company is always a unique combination of its constraints, use case and data, and this is why companies should have some of their data scientists in-house,” said Guttmann. “To materialize the potential of those data scientists, a good software infrastructure needs to be put in place, doing the heavy lifting in operations and letting the data science team concentrate on the actual value they bring to the company.”
A lean innovation for business requirements
AIaaS is a proven approach that facilitates all aspects of AI innovation. The platform provides an all-in-one solution for modern business requirements, from ideating on how AI can provide value to actual, with a scaled implementation across a business as a target – to tangible outcomes in a matter of weeks.
AIaaS enables a structured, beneficial way of balancing data science, IT and business consulting competencies, as well as balancing the technical delivery with the role of ongoing change management that comes with AI. It also decreases the risk of AI innovation, improving time-to-market, product outcomes and value for the business. At the same time, AIaaS provides organizations with a blueprint for AI going forward, thereby accelerating internal know-how and ability to execute, ensuring an agile delivery framework alignment, and transparency in creating the AI.
“AIaaS platforms can quickly scale up or down as needed to meet changing business needs, providing organizations with the flexibility to adjust their AI capabilities as needed,” Yashar Behzadi, CEO and founder of Synthesis AI, told VentureBeat.
Behzadi said AIaaS platforms can integrate with a wide range of other technologies, such as cloud storage and analytics tools, making it easier for organizations to leverage AI in conjunction with other tools and platforms.
“AIaaS platforms often provide organizations with access to the latest and most advanced AI technologies, including machine learning algorithms and tools. This can help organizations build more accurate and effective machine learning models because AIaaS platforms often have access to large amounts of data,” said Behzadi. “This can be particularly beneficial for organizations with limited data available for training their models.”
Current market adoption and challenges
AIaaS platforms can process and analyze large volumes of text data, such as customer reviews or social media posts, to help computers and humans communicate more clearly. These platforms can also be used to build chatbots that can handle customer inquiries and requests, providing a convenient way for organizations to interact with customers and improve customer service. Computer vision training is another large use case, as AIaaS platforms can analyze and interpret images and video data, such as facial recognition or object detection; this can be inculcated in various applications, including security and surveillance, marketing and manufacturing.
“Recently, we’ve seen a boom in the popularity of generative AI, which is another case of AIaaS being used to create content,” said Behzadi. “These services can create text or image content at scale with near-zero variable costs. Organizations are still figuring out how to practically use generative AI at scale, but the foundations are there.”
Talking about the current challenges of AIaaS, Behzadi explained that company use cases are often nuanced and specialized, and generalized AIaaS systems may need to be revised for unique use cases.
“The inability to fine-tune the models for company-specific data may result in lower-than-expected performance and ROI. However, this also ties into the lack of control organizations that use AIaaS may have over their systems and technologies, which can be a concern,” he said.
Behzadi said that while integration can benefit the technology, it can also be complex and time-consuming to integrate with an organization’s existing systems and processes.
“Additionally, the capabilities and biases inherent in AIaaS systems are unknown and may lead to unexpected outcomes. Lack of visibility into the ‘black box’ can also lead to ethical concerns of bias and privacy, and organizations do not have the technical insight and visibility to fully understand and characterize performance,” said Behzadi.
He suggests that CTOs should first consider the organization’s specific business needs and goals and whether an AIaaS solution can help meet these needs. This may involve assessing the organization’s data resources and the potential benefits and costs of incorporating AI into their operations.
“By leveraging AIaaS, a company is not investing in building core capabilities over time. Efficiency and cost-saving in the near term have to be weighed against capability in the long term. Additionally, a CTO should assess the ability of the more generalized AIaaS offering to meet the company’s potentially customized needs,” he said.
What to expect from AI-as-a-service in 2023
Behzadi says that AIaaS systems are maturing and allowing customers to fine-tune the models with company-specific data, and this expanded capability will enable enterprises to create more targeted models for their specific use cases.
“Providers will likely continue to specialize in various industries and sectors, offering tailored solutions for specific business needs. This may include the development of industry-specific AI tools and technologies,” he said. “As foundational NLP and computer vision models continue to evolve rapidly, they will increasingly power the AIaaS offerings. This will lead to faster capability development, lower cost of development, and greater capability.”
Likewise, Guttmann predicts that we will see many more NLP-based models with simple APIs that companies can integrate directly into their products.
“I think that surprisingly enough, a lot of companies will realize they can do more with their current data sScience teams and leverage AIaaS for the ‘simple tasks.’ We have witnessed a huge jump in capabilities over the last year, and I think the upcoming year is when companies capitalize on those new offerings,” he said.
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