Artificial Intelligence
The Global Machine Learning Model Operationalization Management (MLOps) Market size is expected to reach $8.5 billion by 2028, rising at a market growth of 38.9% CAGR during the forecast period
New York, Jan. 25, 2023 (GLOBE NEWSWIRE) — Reportlinker.com announces the release of the report “Global Machine Learning Model Operationalization Management Market Size, Share & Industry Trends Analysis Report By Component, By Vertical, By Organization size, By Deployment Mode, By Regional Outlook and Forecast, 2022 – 2028” – https://www.reportlinker.com/p06412056/?utm_source=GNW
In addition, aligning models with business demands and regulatory standards is simpler.
MLOps is gradually becoming a stand-alone method for managing the ML lifecycle. It covers every lifecycle stage, including data collection, model building (using the software development lifecycle and continuous integration/delivery), deployment, orchestration, health, governance, diagnostics, and business metrics.
Machine learning technology solutions are being aggressively adopted by businesses to improve the customer experience and support maximizing profit. Market participants are implementing advanced data processing and integration strategies to gather insights and get a competitive edge over rivals. The use of MLOps in enterprises is still in its infancy.
As people become more aware of the advantages of doing so, there will likely be lucrative chances for market expansion. The demand for cutting-edge solutions for improved data management is fueled by the expanding usage of data science technologies for improvements in computing power, artificial intelligence, and system learning.
The well-known industry verticals, such as retail, healthcare, education, telecommunication, manufacturing, and financial institutions, have a significant demand for machine learning. Standardized models and workflows are made possible with the assistance of ML Ops. Additionally, it facilitates the simple implementation of machine learning technology anywhere, which is the primary factor in enterprises’ high preference for them.
COVID-19 Impact Analysis
The COVID-19 pandemic is anticipated to be aided by artificial intelligence technology. Several nations are using population surveillance techniques made possible by machine learning and artificial intelligence to track and trace COVID-19 cases. For instance, researchers in South Korea use geo-location information and surveillance camera footage to monitor coronavirus cases. In addition, data scientists use machine intelligence algorithms to anticipate the location of the next outbreak and notify the appropriate authorities, allowing for real-time illness tracking. This has permitted technologically advanced nations to put a speed breaker on the spread of the virus. Such active endeavors are projected to increase the demand for machine intelligence solutions during the upcoming period.
Market Growth Factors
ML should be standardized for efficient teamwork.
Manual data collection and reprocessing are inefficient and may yield unacceptable results. MLOps aids in automating the entire workflow of ML models. This comprises data collection, the model creation, testing, retraining, and deployment. MLOps assist businesses in reducing errors and saving time. For the company-wide adoption of ML models, IT and business professionals and data scientists and engineers are involved in cooperation.
Use of Machine Leading Expanded in The Financial Sector
Financial institutions possess a vast amount of client information. They may collect information on purchases, spending habits, platform usage, and geo-locational preferences in addition to standard banking information, such as bank account balances, to create a 360-degree image of the consumer. This enables the bank to offer goods and services that are particularly tailored to the customer’s requirements and preferences. Therefore, the growing use of ML in the financial industry will fuel the expansion of the MLOps market.
Market Restraining Factors
Lack of Expertise
While more SMBs in the machine learning as a service industry use cloud-based services, the time-consuming machine learning integration process will become significantly less time-consuming. It helps to enhance an organization’s efficiency without recruiting human resources by avoiding repetitive work. Organizations need now utilise MLOps in data management to collect and integrate the enormous volumes of data from several internal and external data sources and unite the data silos.
Components Outlook
Based on components, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Platform and Services. In 2021, the services segment recorded a sizable revenue share. MLOps solutions are being adopted by businesses worldwide to strengthen their customer interaction, brand recognition, and marketing initiatives. Organizations can effortlessly engage consumers, communicate more effectively, and broaden their reach using MLOps marketing tools.
Deployment Mode Outlook
Based on deployment mode, the Machine Learning Model Operationalization Management (MLOps) Market is classified into On-Premises and Cloud. The cloud category had the most revenue share in the market in 2021. To boost employee productivity, the cloud-based system enables worldwide IT task outsourcing. Three other types of cloud computing exist private, public, and hybrid. The public cloud’s rising popularity is primarily due to its numerous organizational advantages, including flexibility and scalability, remote access, simplicity, speedier installation, and many other benefits.
Organization Size Outlook
Based on organization size, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into Large Enterprises and SMEs based on Organization Size. In 2021, the small and medium-sized business segment obtained a sizeable revenue share. This is because machine learning adoption enables SMEs to optimize their processes on a limited budget. Shortly, it is anticipated that AI and machine learning will be the key technologies that let SMEs access digital resources and save money on ICT.
Vertical Outlook
Based on vertical, the Machine Learning Model Operationalization Management (MLOps) Market is categorized into BFSI, Retail and eCommerce, Government and Defense, Healthcare and Life Sciences, Manufacturing, Telecom, IT and ITeS, Energy, and Utilities, Transportation and Logistics, and Others. The BFSI sector produced the highest revenue share in the market in 2021. However, most banks also experience considerable difficulties managing inert models, particularly in settings where application deployments could be more active and influential. MLOps, which essentially applies DevOps techniques and methods to machine learning, can assist banks in swiftly and effectively addressing some of these issues.
Regional Outlook
Based on geography, the Machine Learning Model Operationalization Management (MLOps) Market is classified into North America, Europe, Asia Pacific, and LAMEA. North America is anticipated to hold the most significant market share during the projection period. By market share, North America is one of the top regions for MLOps. MLOps in this region are expanding due to the use of ML technology by nations like the US and Canada in various application fields. The US is regarded as one of the key contributors to North American MLOps market.
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning Model Operationalization Management (MLOps). Companies such as Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Hewlett-Packard enterprise Company are some of the key innovators in Machine Learning Model Operationalization Management (MLOps).
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
Recent Strategies Deployed in Machine Learning Model Operationalization Management (MLOps)
Partnerships, Collaborations & Agreements
Sep-2022: Domino Data Lab has collaborated with Nvidia, a Chipmaker company, and NetApp, a data management and storage system provider. This collaboration is aimed to advance the latest solutions and reference architecture that would help data science and machine learning workloads that are operating in muli cloud and hybrid systems.
Mar-2022: Amazon collaborated with Virginia Tech, a public land-grant research university, and launched an initiative for ML and AI research. This collaboration would allow doctoral students who have applied for Amazon fellowships and are performing ML and AI research. Additionally, this would help the efforts of faculty members engaged in the field of research.
Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real-time to enhance the satisfaction of customers.
Mar-2021: Amazon partnered with Hugging Face, a company that develops tools for building applications using machine learning. Through this Partnership, the company would ease the use of Machine Learning models for organizations and provide advanced NLP features in comparatively lesser time.
Feb-2021: Amazon Web Services entered into a partnership with Salesforce, a cloud-based software company. The partnership enabled us to utilize a complete set of Salesforce and AWS capabilities simultaneously to rapidly develop and deploy new business applications that facilitate digital transformation. Salesforce also embedded AWS services for voice, video, artificial intelligence (AI), and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.
Product Launches and Product Expansions:
Dec-2022: Alteryx, Inc. launched Alteryx Machine Learning. This newly launched product consists of Time Series enhancements which would broaden the predictive power of the company’s machine learning product. The product also includes a user interface (UI) update with new model evaluation abilities creating the process of model development highly simple and intuitive.
Apr-2022: Hewlett Packard released Machine Learning Development System (MLDS) and Swarm Learning, their new machine learning solutions. The two solutions are focused on simplifying the burdens of AI development in a development environment that progressively consists of large amounts of protected data and specialized hardware. The MLDS provides a full software and services stack, including a training platform (the HPE Machine Learning Development Environment), container management (Docker), cluster management (HPE Cluster Manager), and Red Hat Enterprise Linux
May-2022: Hewlett Packard launched HPE Swarm Learning and the new Machine Learning (ML) Development System, two AI and ML-based solutions. These new solutions increase the accuracy of models, solve AI infrastructure burdens, and improve data privacy standards. The company declared the new tool a “breakthrough AI solution” that focuses on fast-tracking insights at the edge, with attributes ranging from identifying card fraud to diagnosing diseases.
Jan-2022: Domino Data Lab unveiled Domino 5.0, the first Enterprise MLOps solution, an end-to-end software suite optimized to run AI workloads with VMWare. This newly launched platform would help the end-to-end data science lifecycle and offer data scientists in using the tools of their choice.
May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that, the engineers can install models for online or batch use cases all on scalable managed infrastructure.
Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.
Acquisitions and Mergers
Jul-2021: DataRobot took over Algorithmia, a machine learning operations platform. The acquisition of Algorithmia would strengthen DataRobot’s position as the preeminent provider of complete solutions in the MLOps space, focused on offering machine learning models into production.
Jun-2021: Hewlett Packard completed the acquisition of Determined AI, a San Francisco-based startup that offers a strong and solid software stack to train AI models faster, at any scale, utilizing its open-source machine learning (ML) platform. Hewlett Packard integrated Determined AI’s unique software solution with its world-leading AI and high-performance computing (HPC) products to empower ML engineers to conveniently deploy and train machine learning models to offer faster and more precise analysis from their data in almost every industry.
May-2021: IBM acquired Waeg, a Salesforce Consulting Partner in Europe. Through this acquisition, IBM would broaden IBM’s suite of Salesforce services and develop IBM’s AI and hybrid cloud strategy. Additionally, this acquisition is based on IBM’s continued investment in Salesforce consulting services to address the growing client requirements for experience-led business transformation and the latest customer engagement strategies supported by machine learning, data, and AI.
Scope of the Study
Market Segments covered in the Report:
By Component
• Platform
• Services
By Vertical
• BFSI
• IT & ITeS
• Manufacturing
• Retail & Ecommerce
• Government & Defense
• Healthcare & Life Sciences
• Telecom
• Energy & Utilities
• Travel & Tourism
• Others
By Organization size
• Large Enterprises
• SMEs
By Deployment Mode
• Cloud
• On-premise
By Geography
• North America
o US
o Canada
o Mexico
o Rest of North America
• Europe
o Germany
o UK
o France
o Russia
o Spain
o Italy
o Rest of Europe
• Asia Pacific
o China
o Japan
o India
o South Korea
o Singapore
o Malaysia
o Rest of Asia Pacific
• LAMEA
o Brazil
o Argentina
o UAE
o Saudi Arabia
o South Africa
o Nigeria
o Rest of LAMEA
Companies Profiled
• Microsoft Corporation
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• Google LLC
• IBM Corporation
• Hewlett-Packard enterprise Company
• Alteryx, Inc.
• Cloudera, Inc.
• DataRobot, Inc.
• Domino Data Lab, Inc.
• H2O.ai, Inc.
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Artificial Intelligence
Data Center Chip Market Size was Valued at USD 11.7 Billion in 2022 and is Expected to Reach USD 45.3 Billion by 2032 at a CAGR of 14.6% | Valuates Reports
BANGALORE, India, July 26, 2024 /PRNewswire/ — Data Center Chip Market By Chip Type (GPU, ASIC, FPGA, CPU, Others), By Data Center Size (Small and Medium Size, Large Size), By Industry Verticals (BFSI, Manufacturing, Government, IT and Telecom, Retail, Transportation, Energy and Utilities, Others): Global Opportunity Analysis and Industry Forecast, 2023-2032.
The Data Center Chip Market was valued at USD 11.7 Billion in 2022, and is estimated to reach USD 45.3 Billion by 2032, growing at a CAGR of 14.6% from 2023 to 2032.
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Major Factors Driving the Growth of Data Center Chip Market
Because of the growing need for data processing and storage solutions brought about by the quick development of cloud computing, artificial intelligence, and big data analytics, the data center chip market is expanding significantly. High-performance chips are necessary for data centers to process massive volumes of data quickly and efficiently. As a result, advances in chip technology, including CPUs, GPUs, and specialist AI processors, have been made. The need for more resilient and scalable data center infrastructure is fueled in part by the expansion of digital services and Internet of Things (IoT) devices. The market is expanding due to key areas including Asia-Pacific, with its investments in technology and fast digital transformation, and North America, with its top tech businesses and vast data center networks.
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TRENDS INFLUENCING THE GROWTH OF THE DATA CENTER CHIP MARKET:
In data centers, Graphics Processing Units (GPUs) are essential for speeding up computing operations and data processing. They are perfect for managing workloads related to artificial intelligence (AI), machine learning, and large-scale data analytics because of their parallel processing capabilities. The need for GPUs in data centers is growing as these technologies become increasingly essential to corporate operations. Businesses are purchasing GPUs in order to increase the effectiveness of their data processing, lower latency, and boost overall performance. The need for data center chips is being driven by the increasing reliance on GPUs for sophisticated computing activities, which is considerably contributing to the market’s rise. This need is further increased by the growing use of AI and machine learning in a variety of sectors, which puts GPUs at the forefront of the data center semiconductor industry.
Compared to general-purpose chips, Application Specific Integrated Circuits (ASICs) provide better performance and efficiency since they are designed specifically for a given application. ASICs are extensively utilized in data centers for specific tasks including networking, data compression, and encryption. ASICs are becoming more and more common as a result of the growth of cloud computing, big data analytics, and blockchain technology, which has increased demand for high-performance, energy-efficient processors. Their capacity to provide tailored performance for certain applications aids data centers in better workload management, power conservation, and operating expense reduction. The market is expanding as a result of the increased preference for ASICs in data centers, which is fueling the need for specialized data center chips.
Large data centers are important users of data center chips; they are run by well-known IT firms and cloud service providers. To manage enormous volumes of data and provide a wide range of services, these facilities need a great deal of processing power and sophisticated computing skills. High-performance data center chips are becoming more and more necessary as a result of the growth of massive data centers and the rising demand for online streaming, cloud services, and digital transactions. These chips are necessary to ensure effective data management, processing, and storage, which helps big data centers fulfill the increasing expectations of its clientele. Large data center proliferation is anticipated to considerably boost the data center chip industry as the digital economy continues to grow.
Data centers are becoming more and more important to the Banking, Financial Services, and Insurance (BFSI) industry as a means of safely and effectively managing high transaction volumes, consumer data, and financial records. The need for sophisticated data center processors is being driven by the sector’s requirement for real-time data processing, high-performance computing, and strong security measures. BFSI organizations may improve their operational efficiency, guarantee data integrity, and deliver superior client services by utilizing data centers fitted with robust chips. The BFSI sector’s need for data center chips is being driven by the increasing use of online banking, digital banking, and financial analytics tools, all of which increase the requirement for sophisticated data center infrastructure.
The market for data center chips is significantly influenced by the cloud computing industry’s explosive growth. There is a growing need for scalable, effective, and high-performance data center infrastructure as more companies move their operations to the cloud. In order to handle enormous volumes of data, facilitate virtualization, and guarantee flawless service delivery, cloud service providers need sophisticated data center chips. Sturdy data center chips are becoming more and more necessary as cloud-based solutions become more and more popular. Benefits like cost savings, flexibility, and scalability are driving this trend. In places like North America and Europe, where cloud adoption rates are high and data center chip demand is rising rapidly, this tendency is especially significant.
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DATA CENTER CHIP MARKET SHARE
In 2022, North America gained a sizable portion of the market.
In 2022, the GPU made up the largest portion of the market share.
Throughout the projection period, large data centers are expected to gain a significant portion.
The BFSI market is anticipated to be one of the most profitable markets.
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Key Companies:
Advanced Micro Devices IncTaiwan Semiconductor Manufacturing Company LimitedBroadcomHuawei Technologies Co LtdIntel CorporationNVidia CorporationSamsung Electronics Co LtdQualcomm Technologies IncGlobalFoundriesARM LIMITED (SOFTBANK GROUP CORP.)Purchase Chapters @ https://reports.valuates.com/request/chaptercost/ALLI-Auto-2B326/Data_Center_Chip_Market
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Artificial Intelligence
Industry 4.0 Market to Surpass USD 513.89 Billion by 2031 with Automation Surge | SkyQuest Technology
WESTFORD, Mass., July 26, 2024 /PRNewswire/ — According to SkyQuest, the global Industry 4.0 Market size was valued at USD 133.05 billion in 2022 and is poised to grow from USD 154.6 billion in 2023 to USD 513.89 billion by 2031, growing at a CAGR of 16.2% during the forecast period (2024-2031).
Industry 4.0 or the fourth industrial revolution emphasizes the use of automation and interconnectivity. Employment of advanced technologies such as artificial intelligence, machine learning, robotics, and connected devices to improve the productivity and efficiency of industries. Rapid digitization and advancements in technology are forecasted to bolster the Industry 4.0 market growth over the coming years. The global Industry 4.0 market is segmented into technology, industry vertical, and region.
Download a detailed overview:
https://www.skyquestt.com/sample-request/industry-4-0-market
Industry 4.0 Market Overview:
Report Coverage
Details
Market Revenue in 2023
$ 154.6 billion
Estimated Value by 2031
$ 513.89 billion
Growth Rate
Poised to grow at a CAGR of 16.2%
Forecast Period
2024–2031
Forecast Units
Value (USD Billion)
Report Coverage
Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
Segments Covered
Technology, Industry and Region
Geographies Covered
North America, Europe, Asia Pacific, Latin America, and Middle East and Africa.
Report Highlights
Internet of Things (IoT) technology takes centerstage for Industry 4.0 adoption
Key Market Opportunities
Adoption of smart manufacturing and additive manufacturing practices
Key Market Drivers
Rising demand for automation across all industry verticals
Segments covered in Industry 4.0 Market are as follows:
TechnologyRobots (Traditional Industrial Robots {Articulated robots, Cartesian Robots, Selective Compliance Assembly Robot Arm (SCARA), Cylindrical Robots, Others}, Collaborative Robots), Blockchain in Manufacturing, Industrial Sensors (Level Sensors, Temperature Sensors, Flow Sensors, Position Sensors, Pressure Sensors, Force Sensors, Humidity & Moisture Sensors, Gas Sensors), Industrial 3D Printing, Machine Vision (Camera {Digital Camera, Smart Camera}, Frame Grabbers, Optics, and LED Lighting, Processor and Software), HMI (Offering {Hardware [Basic HMI, Advanced Panel-based HMI, Advanced PC-based HMI, Others], Software [On-premises HMI, Cloud-based HMI], Services}), Configuration ({Embedded HMI, Standalone HMI}, Technology {Motion HMI, Bionic HMI, Tactile HMI, Acoustic HMI}, End-user Industry {Process industries [Oil & Gas, Food & beverages, Pharmaceuticals, Chemicals, Energy & power, Metals & mining, Water & wastewater, Others], Discrete industry [Automotive, Aerospace & defense, Packaging, Medical devices, Semiconductor & electronics, Others]}), AI In Manufacturing (Offering {Hardware [Processor MPU, GPU, FPGA, ASIC, Memory, Network], Software [AI solutions- | On-premises, Cloud |, AI platform- | Machine learning framework, Application program interface |], Services [Deployment & integration, Support & maintenance]}, Technology {Machine learning [Deep learning, Supervised learning, Reinforcement learning, Reinforcement learning, Others], Natural language processing [Context-aware computing, Computer vision]}, Application {Predictive maintenance and machinery inspection, Material movement, Production planning, Field services, Quality control, Cybersecurity, Industrial robots, Reclamation}, Digital Twin {Technology [Internet of Things (IOT), Blockchain, Artificial intelligence & machine learning, Artificial intelligence & machine learning, Big data analytics, 5G], Usage Type [Product digital twin, Process digital twin, System digital twin], Application [Product design & development, Performance monitoring, Predictive maintenance, Inventory management, Business optimization, Others]}, Automated Guided Vehicles (AGV) {Type [Tow vehicles, Unit load carriers, Pallet trucks, Assembly line vehicles, Forklift trucks, Others], Navigation Technology [Laser guidance, Magnetic guidance, Inductive guidance, Optical tape guidance, Vision guidance, Others]}, Machine Condition Monitoring {Monitoring Technique [Vibration monitoring, Embedded systems, Vibration analyzers and meters, Thermography, Oil analysis, Corrosion monitoring, Ultrasound emission, Motor current analysis], Offering [Hardware – Vibration sensors, Accelerometers, Tachometers, Infrared sensors, Spectrometers, Ultrasound detectors, Spectrum analyzers, Corrosion probes], Software [Data integration, Diagnostic reporting, Order tracking analysis, Parameter calculation], Deployment Type [On-premises deployment, Cloud deployment], Monitoring Process [Online condition monitoring, Portable condition monitoring]})IndustryManufacturing, Automotive, Energy, Medical, Semiconductor & Electronics, Food & Beverage, Oil & Gas, Aerospace, Metals & Mining, Chemicals, and OthersRequest Free Customization of this report:
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Internet of Things (IoT) Technology to Remain Indispensable for Industry 4.0
Internet of Things (IoT) remains the most crucial technology in global Industry 4.0 market growth owing to its role in interconnectivity and automation across different verticals. Advancements in connectivity technologies and rising use of automation in different industry verticals are also estimated to help this sub-segment gain an impressive market share. Surging demand for predictive maintenance will also boost the adoption of IoT technology in the long run.
Advanced robotic technologies are also slated to gain traction in the Industry 4.0 market. Growing acceptance of robots and high investments in advancements of robotic technologies are also slated to create new opportunities for providers of advanced robotics in the Industry 4.0 market. The low margin of error and the immense scope of automation are key benefits of robotics that help this sub-segment flourish.
Artificial intelligence (AI) will be another popular technology in the Industry 4.0 world going forward. Increasing demand for continuous monitoring, real-time analytics, and predictive maintenance are slated to help the demand for artificial intelligence in the future. The rising use of IoT devices will also boost the demand for cloud computing technology in the long run.
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Manufacturing Vertical to Spearhead Industry 4.0 Market Development
The manufacturing vertical is estimated to be at the forefront when it comes to Industry 4.0 adoption. The surge in use of robotics, advanced technologies, and smart manufacturing practices sets the tone for Industry 4.0 in this industry vertical. High emphasis on improving manufacturing efficiency, reducing downtime, and maximizing profits are all contributing to the high market share of this sub-segment.
The automotive industry is another vertical where Industry 4.0 market players could invest to get good returns. The high adoption of advanced robotics and other smart manufacturing technologies to maximize production allows this sub-segment to become a crucial one for Industry 4.0 providers. The aerospace and defense industry vertical also shows a lot of promise for Industry 4.0 companies going forward. Growing demand for advanced manufacturing techniques and technologies to create complex aerospace components is helping Industry 4.0 market growth via this segment.
The oil & gas industry is also estimated to embrace Industry 4.0 trend with open hands as they try to improve their operations and promote better resource utilization. High demand for predictive maintenance to reduce downtime and the growing adoption of digital oilfield solutions are estimated to bolster Industry 4.0 market development in the long run.
To sum it up, the application scope for Industry 4.0 is endless as automation and digitization pick up pace around the world. High investments in development of IoT and AI technologies will create better opportunities for Industry 4.0 companies in the future. The manufacturing industry will remain the top revenue generating sub-segment and more opportunities for aerospace, automotive, and oil & gas verticals will be seen over the coming years.
Related Report:
Digital Twin Market
Cyber Security Market
Artificial Intelligence (AI) Market
Internet Of Things (IoT) Market
Machine Learning Market
About Us:
SkyQuest is an IP focused Research and Investment Bank and Accelerator of Technology and assets. We provide access to technologies, markets and finance across sectors viz. Life Sciences, CleanTech, AgriTech, NanoTech and Information & Communication Technology.
We work closely with innovators, inventors, innovation seekers, entrepreneurs, companies and investors alike in leveraging external sources of R&D. Moreover, we help them in optimizing the economic potential of their intellectual assets. Our experiences with innovation management and commercialization has expanded our reach across North America, Europe, ASEAN and Asia Pacific.
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Artificial Intelligence
Generative AI Cybersecurity Market worth $40.1 billion by 2030 – Exclusive Report by MarketsandMarkets™
CHICAGO, July 26, 2024 /PRNewswire/ — The Generative AI cybersecurity Market is anticipated to experience substantial expansion, ascending from a value of USD 7.1 billion in 2024 to a substantial worth of USD 40.1 billion by the year 2030, according to a new report by MarketsandMarkets™. This growth trajectory reflects a robust compound annual growth rate (CAGR) of 33.4% over the forecast period.
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350 – Tables 60 – Figures450 – Pages
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Scope of the Report
Report Metrics
Details
Market size available for years
2019–2030
Base year considered
2023
Forecast period
2024–2030
Forecast units
USD (Million)
Segments Covered
Offering, Generative AI-based Cybersecurity, Cybersecurity for Generative AI, Security Type, End-user, and Region
Geographies covered
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
Companies covered
Microsoft (US), IBM (US), Google (US), SentinelOne (US), AWS (US), NVIDIA (US), Cisco (US), CrowdStrike (US), Fortinet (US), Zscaler (US), Trend Micro (Japan), Palo Alto Networks (US), BlackBerry (Canada), Darktrace (UK), F5 (US), Okta (US), Sangfor (China), SecurityScorecard (US), Sophos (UK), Broadcom (US), Trellix (US), Veracode (US), LexisNexis (US), Abnormal Security (US), Adversa AI (Israel), Aquasec (US), BigID (US), Checkmarx (US), Cohesity (US), Credo AI (US), Cybereason (US), DeepKeep (Israel), Elastic NV (US), Flashpoint (US), Lakera (US), MOSTLY AI (Austria), Recorded Future (US), Secureframe (US), Skyflow (US), SlashNext (US), Snyk (US), Tenable (US), TrojAI (Canada), VirusTotal (Spain), XenonStack (UAE), and Zerofox (US).
This dramatic surge is being fueled by a number of causes. The primary growth driver is the enhancement of existing cybersecurity tools through generative AI algorithms by improving anomaly detection, automating threat hunting and penetration testing, and providing complex simulations for security testing purposes. These techniques enable various cyber-attack scenarios that can be simulated using the Generative Adversarial Networks (GANs), thus enabling the development of better preparedness and response strategies. On the other hand, it requires special cyber security tools to protect generative AI workloads against unique vulnerabilities such as adversarial attacks, model inversions and LLM poisoning. These tools include differential privacy and secure multi-party computation that are integrated into AI systems for training and deployment data protection purposes.
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Generative AI apps security segment will account for largest market share during the forecast period.
The cybersecurity landscape is rapidly changing for generative AI apps, which are already making their way into chatbots, content creation tools like word processors, and personalized recommendation systems. According to McAfee, 55% of these programs have had security breaches. This highlights the dire need for stronger protective measures from unauthorized access. Several generative AI applications that use adversarial techniques to force the desired reaction out of intelligent machines.
Therefore, there is a pressing demand in the number of developers who ensure that such machines are made more robust through techniques like adversarially trained models and resistant architectures. Finally, the usage of secure enclaves plus hardware-based security measures is growing off late, mainly aimed at safeguarding vulnerable AI computations from being tampered with. For instance, OpenAI has very strict security rules meant to protect GPT models thereby ensuring data integrity and user privacy.
By end-user, government & defense sector is poised to account for larger market share in 2024.
Government as well as defense industries are increasingly resorting to generative AI for cyber security purposes due to the urgency of protecting sensitive information and national security. According to a recent CSIS report, AI is being integrated into the cybersecurity framework of 43% of government agencies which resultantly improves their ability to identify and counter threats. As an example, the United States Department of Defense has started using artificial intelligence (AI) based security solutions backed by generative AI that can create fictitious cyber-attacks, thereby providing them with enhanced preparedness against advanced types of threats.
This technology also helps these sectors handle and analyze large volumes of data more effectively, giving valuable insights that will enable them prevent or mitigate cyber threats. This trend demonstrates an increasing reliance on generative AI in fortifying cyber security measures so as to ensure that critical infrastructure and sensitive data remain secure in today’s intricate digital landscape.
By region, North America to hold the largest share by market value in 2024.
In 2024, North America will be the leading region based on market share due to its excellent technology infrastructure, substantial investments in AI-enabled cybersecurity and the presence of key players. Major cyber security research universities and tech companies such as Google, AWS, CrowdStrike, SentinelOne and IBM are present in this area, pushing them on the forefront of potent risk management technologies and generative AI tools for threat detection. For example, IBM’s security platform powered by AI has improved detection rates for threats up by 40%, thus proving the relevance of AI technology to enhancing cybersecurity.
Moreover, legislative instruments such as Cybersecurity Information Sharing Act (CISA) are being put in place to promote advanced cybersecurity technologies. As internet attacks continue getting more complicated, North American enterprises prefer generative artificial intelligence (AI), so as to enhance their safety measures pertaining to personal data and digital infrastructure.
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Top Key Companies in Generative AI cybersecurity Market:
The major players in the generative AI cybersecurity market include Palo Alto Networks (US), AWS (US), CrowdStrike (US), SentinelOne (US), and Google (US), along with SMEs and startups such as MOSTLY AI (Austria), XenonStack (UAE), BigID (US), Abnormal Security (US), and Adversa AI (Israel).
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