Top 10 Industry Trends in Machine Learning as a Service for 2022

Industry Insights

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:

  1. 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.

  1. ML Algorithms for Telecom

ML algorithms are helping to perform remedial actions and notify the network administrator about anomalies & network issues.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. Cloud-in-a-Pocket Approach

Growing usage of smartphones is enabling personalized services for data transfer by eliminating the need for centralized company servers.

  1. 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.


“Riddhi Sawant is an experienced and professional technical writer who provides actionable insights about current developments in a range of industries. She is certified in Digital Marketing and has specialized in Mass Communication & Advertising. The market research universe excites her since every industry is witnessing a sea change with technological and digital transformation.”

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