Srikanth
Artificial Intelligence As a Service (AIaaS) enables companies to utilize AI for certain use cases and reduce risks and costs at the same time. This can include sampling several public cloud platforms to test various machine learning algorithms. The application of AIaaS crosses all sectors and because of the features associated with each different service provider, the customer, that is, the company can choose from a large number of options.
In this article, we see some of the benefits that businesses can get by using AIaaS and the most popular service providers in India.
Why use AIaaS for business solutions?
Cost and time savings: One of the biggest advantages associated with AIaaS is the reduced cost and time of implementing solutions. By providing ready infrastructure and algorithms that have been trained before, this saves business from setting up their own applications. While previous business solutions must develop their own applications, in this case, all that the company needs to do is contact the service provider.
Because AIaaS is built on an existing cloud framework by training machine learning models and then being used for VMs and containers for inference. Without creating a custom machine learning model, service providers utilize the basic infrastructure that should be built on IaaS (Infrastructure as a Service) and SaaS (Software as a Service). This is another major advantage because it reduces investment risk and increases strategic flexibility.
Usability: With AWS, Microsoft and Google dominate this sector, in an effort to become more than just a service provider, companies also compete with each other to build tools for data scientists and developers. Coupled with this is a step towards open-source platforms such as TensorFlow, Caffe and AutoML allowing developers to build custom AI models.
Scalability: This will allow companies to grow by starting small and allowing them to gradually increase their AI operations over time.
AIaaS type:
Machine learning framework: This tool allows developers to build their own models and learn from existing data sets. This will allow building machine learning tasks without the big data environment requirements.
Third party API: This is made to improve the functionality in existing applications. NLP, computer vision, translation, knowledge mapping, emotion detection are some common options for API.
AI-powered bots: Chatbots that use natural language processing capabilities (NLP) to mimic language patterns by learning from human conversation are a common type of AIaaS.
ML services that are fully managed: It uses drag and drop tools, cognitive analysis, and data models that are specifically created to produce richer machine learning values.
Key players in India:
With cloud being the main vertical, the adoption rate for AIaaS has been quite strong in India with most companies making digital switches with cloud adoption. According to a recent study, in 2018 India’s public cloud revenues grew to 37.5% with major service providers being Amazon, Microsoft and Google
We see some big names with product offers for AIaaS.
AWS: Amazon’s in-house AI is currently available on AI and the company will soon open a source of Deep Scalable Sparse Tensor Network Engine (DSSTNE) that can strengthen the capabilities of Amazon customers’ recommendations.
Google: Google Cloud Platform contains a number of AI capabilities such as speech recognition, translation, predictive analysis, and identification of image content. Aside from open-source TensorFlow, the company has also issued Springboard, which allows companies to use Google’s AI-based search interface to extract information from within Google’s product groups.
IBM: Aimed at developers, IBM Developer Cloud helps developers to incorporate Watson intelligence into the application and also train and manage data in the cloud.
Microsoft: The company issued the Microsoft Distributed Machine Learning Tool (DMTK) for researchers and practitioners to study large models of large amounts of data. It also helps users to run multiple applications at once.
With the market for web APIs and cloud APIs witnessing a steady increase, while the NLP market is estimated to reach $ 21 billion by 2025, adoption of AI and other new age technologies in both the private and public sectors has become a reality in India.
However, to produce the true value of its application, there is a great need for Indian companies to use AIaaS carefully after thorough research for better ROI and scalability.
