Trends and Future Prospects for the Global Machine Learning Market
RELEASE DATE: Jul 2024 Author: Spherical Insights Request Free Sample
Overview
Machine Learning (ML) is the field of computer science and Artificial Intelligence (AI). The combination of data and algorithms is used to recreate human learning and gradually enhance the accuracy of the system. The Global Machine Learning Market Size was valued at USD 14.91 billion in 2021 and is expected to reach at a CAGR of 38.1% from 2021 to 2030. The worldwide market is expected to reach around USD 302.62 billion by 2030. ML is being used at a large scale in a variety of sectors, including computer vision, huge language models, email filtering, medicine, speech recognition, and agriculture. The market witnessed substantial potential due to the adoption of ML by small and medium-sized businesses (SMEs). The growing need for automation and optimization across industries, the availability of data, and the development of computing power are market growth contributors. The proliferation of cutting-edge computing and the Internet of Things (IoT), generate enormous volumes of data that can be examined using machine learning algorithms. Further, the integration of machine learning with other technologies like computer vision and natural language processing is leveraging market opportunities in the coming years.
Marketing Excellence: Strategies of Machine Learning Industry
The machine learning market is extremely competitive, with major firms competing for market share based on their distinct selling points. Merative, a new data company, formed from IBM’s healthcare analytics assets. The company would combine these assets to help deliver value across healthcare ecosystem, serving clients in life sciences, provider, imaging, health plan, employer, and government health and human services sectors. Google’s acquisitions of DeepMind, an artificial intelligence company, and Nest, a smart home technology provider, are prime examples of its interest in acquiring innovative technologies.
Innovations
The integration of NVIDIA AI Enterprise software into Microsoft’s Azure Machine Learning helps enterprises accelerate AI initiatives. Google's AI initiatives have a transformative impact on various aspects from revolutionizing research and healthcare to redefining human-machine interaction and transportation. Google Brain, DeepMind, Waymo, and Google Assistant, the company has been at the forefront of AI innovation.
Insights and Projections: Global Machine Learning Market Analysis
The report provides a summary of the global top trends and future prospects for the global machine learning market based on historical data sets (current trends, industry statistics, and emerging trends shaping the machine learning industry’s future, preliminary estimates from 2020-2021, 2022, and 2023), as well as projections for the global machine learning trends and future prospects for global regions in 2024 and 2025. According to Spherical Insights LLP, this data-driven research offers users an industry-wide view of emerging technologies and innovative insights to help them make critical business decisions.
Growth Factors and Emerging Trends in the Machine Learning Industry
- Growing need for automation and optimization across industries.
The incorporation of automation aims to lessen the manual effort required for building and implementing models. Automated machine learning (AutoML) platforms are getting more popular, enabling non-experts to leverage machine learning capabilities and accelerate model creation. Additionally, deep learning, a type of machine learning that makes use of multiple-layer neural networks getting better. The advancements in computer vision, natural language processing, and speech recognition are made possible by deep learning. Sentiment analysis and chatbots are the common example of automation. Machine learning’s technology has ability to automate corporate processes simply and effectively.
- Application of ML in various sectors
Various applications of ML have been frequently in the healthcare, banking, and retail sectors. The use of ML by small and medium-sized businesses (SMEs). The use of ML in medical diagnostics is revolutionizing the healthcare industry, for example, an algorithm created by Google’s DeepMind Division for identifying retinal pictures of eye disorders such as diabetic retinopathy. This benefitted with various advantages like early detection, timely treatment, and a decrease in the amount of work of medical professionals. Further, application in medication creation, illness outbreak prediction, and personalized medicine. Machine learning is widely used in the cybersecurity sector, where AI technologies are used to detect cyber threats, fight cybercrime, and improve antivirus software.
Opportunities and Future Prospects in the Machine Learning Industry
ML focuses on algorithms that allow computers to learn and make predictions from data. Natural language processing enables machines to understand & generate human language, and computer vision focuses on processing & understanding visual information. The advancements would transform the technologies in various sectors like healthcare, retail, manufacturing, finance, transportation, and others, offering market opportunities. The interpretation and generation of human-like text, facilitating medical transcription, clinical documentation, and patient communication by using NLP-enabled AI software. Developers can now effectively and affordably satisfy clients' needs as machine learning is capable of handling multidimensional data, even in a dynamic environment. Generated suggestions and recommendations, pattern recognition has become crucial in the e-commerce and retail industries.
List of Key Companies in the Machine Learning Industry
- IBM Corporation
- SAP SE
- Oracle
- Microsoft Corporation
- Amazon, Inc.
- Intel Corporation
- SAS Institute Inc.
- Baidu Inc.
- Google Inc.
- H2o.AI
- Hewlett Packard Enterprise Development LP
- International Business Machines Corporation
- Others
Top Players in the Machine Learning Industry
Microsoft Corporation
Headquarters: Redmond, Washington, United States
Microsoft Corporation is a leading developer of computer software, operating systems, cloud computing, and artificial intelligence applications. The company provides a vast range of ML solutions and services under its Azure Machine learning. With the help of number of pre-built machine learning models, businesses may create, train, and use customized models for a variety of uses, such as computer vision and natural language processing.
The company’s new generative AI solution—Microsoft Security Copilot—combined with our massive data advantage and end-to-end security, all built on the principles of Zero Trust, creates a flywheel of protection to change the asymmetry of the digital threat landscape and favor security teams in this new era of security.
Google Inc.
Headquarters: Mountain View, California, United States
Google is a multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). Google leverages the power of machine learning to enhance user experiences and drive business growth.
Google, the tech giant synonymous with innovation and excellence, owes much of its success to its strategic integration of machine learning algorithms across its diverse array of products and services. PageRank Algorithm, DeepMind’s AlphaGo, BERT, RankBrain, TensorFlow (developed by Google Brain), Word2Vec, Google Photos’ Object Recognition, Google Translate’s Neural Machine Translation (NMT), AutoML, and Google Assistant are machine learning algorithms of Google.
SAP SE
Headquarters: Walldorf, Germany
SAP SE (SAP) is a provider of enterprise application software and related services. SAP customers generate 87% of total global commerce. Machine, Internet of Things (IoT), and advanced analytics technologies help turn customers’ businesses into intelligent enterprises.
SAP Business AI is a revolutionary technology that helps customers bring out their best by building cutting-edge AI innovation directly into the systems that power much of the world’s business. SAP announced the ongoing extensive integration of Joule- its natural-language, generative AI copilot- throughout the company’s technology platform and portfolio of business applications.
Recent Development
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In October 2023, Microsoft introduced new data and AI solutions to help healthcare organizations unlock insights and improve patient and clinician experiences.
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In May 2023, NVIDIA collaborated with Microsoft to accelerate Enterprise-Ready Generative AI. NVIDIA AI Enterprise integration with Azure Machine Learning provides an end-to-end cloud platform for developers to build, deploy, and manage AI applications for large language models.
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In May 2023, IBM at its annual Think conference, announced IBM Watsonx, a new AI and data platform to be released that would enable enterprises to scale and accelerate the impact of the most advanced AI with trusted data.
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In June 2022, Oracle Cloud Infrastructure Expands Distributed Cloud Services with OCI Dedicated Region and Previews Compute Cloud@Customer.
- In June 2022, the Object Management Group (OMG) announced Responsible Computing (RC), a new consortium comprised of technology innovators working together to address sustainable development goals.
- In January 2022, digital experience company Acquia announced advanced retail machine learning models for the Acquia Customer Data Platform (CDP), designed to increase customer lifetime value.
- In May 2021, Google Cloud announced the general availability of Vertex AI, a managed machine learning (ML) platform that allows companies to accelerate the deployment and maintenance of artificial intelligence (AI) models.
- In February 2020, Oracle announced the Oracle Cloud Data Science Platform. The platform was designed to assist businesses in collaboratively training, building, deploying, and managing ML models which enhance the performance of data science programs.
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