Machine learning (ML) technology is playing an instrumental role in better understanding of the SARS-CoV-2 infection by ingesting large volumes of data into computer systems that help to identify patterns in its spread amongst the common public. But can machine learning as a service hold promising potentials in the BFSI, telecom and other sectors? Let’s find out!
Here are top 10 industry trends and innovations in machine learning as a service for 2022 that are associated with both challenges and opportunities:
- Chatbots for BFSI Applications
Though chatbots help to resolve more than 75-80% customer queries and frequently asked questions (FAQs), there is a continuous need for upgradation in these systems to enable a more humanized interaction with customers.
- ML Algorithms for Telecom
ML algorithms are helping to perform remedial actions and notify the network administrator about anomalies & network issues.
- ML for Automobiles
Since ML computers help to mimic human intelligence, electrification of automobiles is creating potentials for predicting street traffic during peak office hours.
- Early Predictions in Healthcare
Early detection of chronic, hereditary and autoimmune diseases has become the need of the hour. Thus, machine learning as a service platforms are anticipated to become increasingly commonplace for routine health checkups of individuals and patients.
- Precision Farming in Agriculture
Since agriculture is largely dependent on uncertainties of climatic conditions, machine learning as a service helps to assess historical data about the weather to predict rains and clouds during plantations. However, there is a need for training of farmers in emerging economies.
- Decision-making in Defense
Due to the ongoing Russia-Ukraine war, machine learning as a service is gaining importance to analyze strategic moves of countries in order to avoid colossal and collateral damages.
- eCommerce
The ever-expanding retail and eCommerce sector is creating a demand for ML technologies to make inroads for last-mile delivery and in new regions to establish sales channels.
- Government
Machine learning algorithms are being known for unparalleled computational capability to extract information from high-dimensional data as well as unstructured data in the government sector.
- Cloud-in-a-Pocket Approach
Growing usage of smartphones is enabling personalized services for data transfer by eliminating the need for centralized company servers.
- Natural Language Generation
ML technology is helping to push boundaries for natural language generation (NLG) such as email writing with the help of Google Smart Compose.
Lastly, continuous R&D investment and proactive troubleshooting is the key to innovate in new machine learning as a service models.
