Impact of Outbreak of COVID-19 on Machine Learning Market– Future Scenario and Business Opportunities Analysis by 2030

ML Accelerating COVID-19 Research and Identifies Patterns in Data

Machine learning (ML) and artificial intelligence (AI) technologies are being deployed in areas of research to fight the COVID-19 pandemic. This research is being extended toward healthcare and agriculture, as currently these are one of the most essential industries during unprecedented times. ML is being highly publicized since this technology enables computers to mimic human intelligence and ingest large volumes of data in order to identify patterns and insights. Thus, companies in the machine learning market are capitalizing on this opportunity to scale customer communications and speed up COVID-19 research for treatment.

The ML technology is bringing quarantine measures and social distancing in place for employees with the help of remote communication, telemedicine, and food security. The proliferation of ML in virtually all end markets is creating value-grab opportunities for companies in the machine learning market.

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Facial Recognition Systems Acquire Prominence in Banking Sector to Prioritize Assistance

The banking sector is able to convert data samples into new interfaces with the help of machine learning. As such, the machine learning market is predicted to cross the value of US$ 600 Bn by the end of 2030. Nowadays, ML has become a prerequisite feature for any intelligent system in the banking sector. ML is facilitating data-driven predictions that help to generate new business opportunities for individual institutions. ML is gaining popularity as the branch of AI that can be used to improve products and customer services in the banking sector.

Automatic reply predictions for email messages and virtual assistants have become customary in the banking industry. Companies in the machine learning market are innovating in facial recognition systems to capitalize on revenue opportunities in the banking sector. These systems help to prioritize individual data before a consumer approaches a banker in an institution for assistance.

Proper Training for Algorithms Improves Computational Feasibility on Large Datasets

The machine learning market is expected to advance at an explosive CAGR of ~27% during the forecast period. However, the implementation of ML models is emerging as a challenge for companies. For instance, computations with Big Data can lead to issues in processing performance of ML models, thus making even trivial operations expensive. Companies in the machine learning market should provide proper training for algorithms in order to improve computational feasibility on very large datasets.

On the other hand, many business owners have stated that time-consuming deployment of ML is predicted to impede market growth. Hence, software companies are educating business owners to deploy ML models on a small scale to check its feasibility. As such, companies are tapping into incremental opportunities in cybersecurity to help business owners identify fraud and prevent phishing attacks.

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ML Helps to Maintain Equilibrium between Energy Supply and Demand

Renewable energy forecasting and predictions regarding power output are some of the useful applications of machine learning. Forecasting has become necessary in the energy and utilities industry, since key renewable energy sources i.e. wind and solar are variable in nature and are dependent on external factors. Germany is known as the poster child for adoption of renewable energy. However, the intensity of solar radiation and wind speed is never uniform. Hence, companies in the machine learning market are proving their software and services to energy and utilities companies to maintain an equilibrium across energy supply and demand.

The machine learning market is dominated by established players such as IBM. However, emerging market players such as the U.K.-based AI company DeepMind, was recently acquired by Google and is posing as a stiff competition to established market players.

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Game-changing Insights on Customer Behavior Benefit Retailers and Telecom Companies

ML models are helping retailers to better understand and analyze customer behavior data, which is collected from various trends, demographics, and preferences. Personalized experiences are becoming increasingly important in the retail industry. With the help of old data, ML models help to predict, which product is worthy of recommendation for customers. The burgeoning growth of the eCommerce sector is acting as a key driver for the expansion of the machine learning market. Retailers are able to analyze digital behavior patterns of customers in order to improve their purchase journey.

Real-time insights powered by ML and AI are grabbing the attention of companies in the IT & telecom sector. Moreover, IoT (Internet of Things) and 5G technologies are fueling the growth of the machine learning market. ML algorithms are being used by telecom companies to track data streaming from a plethora of network interfaces.

Automakers and Digital Operators Adopt ML Tools to Enable Productive Consumer Involvement

Apart from retail and banking, companies in the machine learning market are broadening their revenue streams in automotive and content moderation applications. The trend of autonomous vehicles is gaining strong business grounds worldwide. It has been found that self-driving cars will help to drastically reduce traffic-related issues. Software engineers are relying on ML algorithms to design autonomous features in self-driving cars in order to make the vehicles more consumer-centric.

High accessibility of the Internet and modern technology have led to the outspread of fake news and disinformation. In such situations, content moderation comes into play where ML platforms are enhancing interactions between humans and AI to enable the content moderation task. Social networking platforms and news outlets are adopting ML models to increase human involvement toward productive work. ML tools help to address and analyze the context of every single frame of video content in order to drive content moderation.

Analysts’ Viewpoint

Healthcare and government institutes are using ML-enabled chatbots for contactless screening of COVID-19 symptoms. With the help of ML tools, banks are using old mathematical challenges to create new computing opportunities. However, issues such as overestimating result delivery and unavailability of data are emerging as hurdles for the widespread adoption of machine learning. Hence, companies in the machine learning market should educate business owners about the use of structured data in order to gain desired outcomes. Renewable energy analysts are using ML models to draw an equilibrium for energy supply and demand. Telecom giants such as AT&T and Vodafone are using machine learning to enhance the quality of their resources.

Machine Learning Market: Overview

  • According to Transparency Market Research’s latest research report on the global machine learning market for the historical period 2018–2019 and the forecast period 2024–2030, the machine learning market is anticipated to reach a value of US$ 604.5 Bn by 2030, expanding at a CAGR of ~27% during the forecast period
  • Machine learning analyzes massive quantities of data. It usually delivers faster and more accurate results in order to identify dangerous risks and profitable opportunities. Merging machine learning (ML) with artificial intelligence and cognitive technologies can make it even more effective in processing large volumes of data and information.
  • Moreover, in the future, applications and tools of machine learning are likely to be adopted by various industry verticals. Technological proliferation and advancement in data generation can trigger the growth of the machine learning market across the globe.

Advent of Self-driving Cars: A Key Driver of Machine Learning Market

  • Self-driving cars have gained incredible popularity in recent years. Developments in Internet of Things (IoT) and AI technology have paved the way for driverless cars. Machine learning is an important component of the centralized electronic control unit (ECU) in a self-driving car. Predicting possible changes in the environment and real-time monitoring of the surroundings are the main functions of machine learning algorithms in self-driving cars.
  • Machine learning algorithms such as unsupervised learning, supervised learning, decision matrix algorithms, and pattern recognition algorithms, are integrated into the functioning of self-driving cars, which, in turn, is likely to accelerate the growth of the machine learning market across the globe.

Key Challenges Faced by Machine Learning Market Players

  • Shortage of skilled professionals may restrain the growth of the machine learning market. Shortage of data scientists with machine learning experience seems to be one of the major concerns in the adoption of machine learning by organizations. Although machine learning software save efforts of data scientists, a professional is required to use data and create the algorithms required for machine learning. Thus, lack of skilled workforce and limited knowledge among end users are expected to hinder the market growth and likely to have a high impact on the market.

Increasing Adoption of Machine Learning Globally

  • Machine learning capabilities are expected to be integrated in more platforms and software in the years to come, enabling organizations to take advantage of them
  • Every company is now becoming a data company, regardless of what an organization does. Traditionally, organizations relied on the availability of structured data to make decisions or predict future outcomes. However, with the explosion of Big Data and machine learning capabilities, it has become possible to analyze unstructured data to make more informed decisions.
  • Rapidity of data generation, availability of huge amount of compute power, and ease of use of new machine learning platforms are projected to lead to more number of applications that make real-time predictions and constantly get better over time
  • Machine learning is expected to have an enormous impact on the environment. It is projected that it will help improve the quality of life of citizens both at home and at work. Additionally, machine learning tools and algorithms will contribute greatly to increase global and industrial competitiveness across all sectors, including large, small, and medium-sized enterprises, and also non-tech industries.

Machine Learning Market: Competition Landscape

  • Detailed profiles of providers of machine learning have been provided in the report to evaluate their financials, key product offerings, recent developments, and strategies
  • Key players operating in the global machine learning market include
  • Amazon Web Services, Inc.,
    • BigML Inc.
    • DeepL GmbH
    • Ersatz Labs Inc.
    • Featurespace Limited
    • FICO
    • Formulate AB
    • German Auto Labs GAL GmbH
    • Google LLC
    • Heuritech
    • Hewlett Packard Enterprise Development LP
    • IBM Corporation
    • InstaDeep Ltd
    • Microsoft Corporation
    • MySales Labs Ltd
    • Oracle Corporation
    • SAP SE
    • SAS Institute Inc.
    • understandAI GmbH

Machine Learning Market: Key Developments

  • Key providers of machine learning, such as Amazon Web Services, Inc., Microsoft Corporation, IBM Corporation, SAP SE, and Google LLC are focusing on the development of machine learning software and services to attract more customers. Some other key developments in the global machine learning market are highlighted below:
  • In April 2019, Google LLC. launched the Google AI Platform. This platform allows machine learning experts to form and run their machine learning models on Google Cloud as well as on premise. The platform is also integrated with machine learning models offered by Google that includes TPUs, Tensor Flow, and TFX tools.
  • In December 2018, Microsoft Corporation launched Azure Machine Learning services, which contain advanced capabilities that are designed to simplify the process of training, building, and deploying machine learning models
  • In November 2017, AWS introduced AWS DeepLens, a deep learning-enabled wireless video camera. AWS DeepLens is integrated with Amazon SageMaker, which helps developers to train their machine learning models over the cloud and deploy them on the DeepLens, which runs the models in real-time on the camera.
  • In the global machine learning market report, we have discussed individual strategies, followed by company profile. The ‘Competition Landscape’ section has been included in the report to provide readers with a dashboard view and company market share analysis of key players operating in the global machine learning market.

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