Artificial Intelligence As a Service (AIaaS) allows enterprises to leverage AI for specific use cases and lower the risk and cost at the same time. This can include a sampling of multiple public cloud platforms to test different machine learning algorithms. The applicability of AIaaS cuts across all sectors and since the features associated with each service providers are different, customers, namely enterprises can opt from a plethora of options.
In this article, we look at some of the advantages that businesses can gain by using AIaaS and the most popular service providers in India.
Why leverage AIaaS for the business solution?
Cost and time efficient: One of the biggest advantage associated with AIaaS has been the reduced cost and time in deploying the solution. By providing a ready infrastructure and pre-trained algorithms, it saves businesses from setting up their own applications. While earlier business solutions had to develop their own application, in this case, all that companies need to do is contact a service provider.
Since AIaaS is built on existing cloud framework by training the machine learning models and then deploying to VMs and containers for inference. Without creating custom machine learning models, service providers make use of the underlying infrastructure which would have otherwise built on IaaS (Infrastructure as a Service) and SaaS (Software as a Service). This is another key advantage as it reduces investment risk and increases strategic flexibility.
Usability: With AWS, Microsoft and Google dominating the sector, in an attempt to be more than just service providers, companies are also competing with each other to build tools for data scientist and developers. Added to this is the move to open-source their platforms like TensorFlow, Caffe and AutoML enabling developers to build a custom AI model.
Scalability: It will allow enterprises to grow by starting small and allowing them to increase their AI operations gradually with time.
Types of AIaaS
Machine learning framework: This tool enables developers to build their own model and learn from an existing pool of data. It will allow building machine learning tasks without the requirement of the big data environment.
Third party APIs: These are created to increase the functionalities in an existing application. NLP, computer vision, translation, knowledge mapping, emotion detections are some of the common options for APIs.
AI-powered bots: Chatbots that uses natural language processing (NLP) capabilities to imitate the language patterns by learning from human conversations are a common type of AIaaS.
Fully-managed ML services: This uses drag-and-drop tools, cognitive analytics and custom-created data models to generate richer machine learning values.
Key Players in India
With cloud being the major vertical, the adoption rate for AIaaS has been quite strong in India with most enterprises making the digital switch with cloud adoption. According to a recent study, in 2018 Indian public cloud revenue grew to 37.5% with major service provider being Amazon, Microsoft and Google
We look at some of the big named with product offerings for AIaaS.
AWS: Amazon’s in-house AI is currently available in AI and the company will soon open-source its Deep Scalable Sparse Tensor Network Engine (DSSTNE) which powers the customer recommendation capabilities of Amazon.
Google: Google Cloud Platform contains a host of AI capabilities like speech recognition, translation, predictive analytics and image content identification. Apart from open-sourcing TensorFlow, the company has also brought out Springboard, allowing enterprises to use Google’s AI-based search interface to retrieve information from within Google products group.
IBM: Aimed at developers, IBM Developer Cloud helps developers to incorporate Watson intelligence into apps and also train and manage data in a cloud.
Microsoft: The company released Microsoft Distributed Machine Learning Toolkit (DMTK) for researchers and practitioners to learn big models from the huge amount of data. It also helps users to run multiple applications at a time.
With the market for web APIs and cloud APIs witnessing a steady surge, while the NLP market is estimated to be $21 billion by 2025, adoption of AI and another new age tech both within the private and public sector has already become a reality in India.
However, to generate true value from its adoption, there is a great need for enterprises Indian to use AIaaS carefully after thorough research for better ROI and scalability.