Data scientists and operations specialists can work together and communicate using MLOps, a set of techniques. Implementing Machine Learning and Deep Learning models in large production environments can be automated while improving quality and streamlining the management process.
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.
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.
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.
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:
• IT & ITeS
• Retail & Ecommerce
• Government & Defense
• Healthcare & Life Sciences
• Energy & Utilities
• Travel & Tourism
By Organization size
• Large Enterprises
By Deployment Mode
• North America
o Rest of North America
o Rest of Europe
• Asia Pacific
o South Korea
o Rest of Asia Pacific
o Saudi Arabia
o South Africa
o Rest of LAMEA
• 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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