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Cloud Machine Learning Operations (MLOps) Market Size to Grow USD 3652.7 Million by 2030 at a CAGR of 44.6% | Valuates Reports

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BANGALORE, India, Sept. 9, 2024 /PRNewswire/ — Cloud Machine Learning Operations (MLOps) Market is Segmented by Type (Platform, Services), by Application (BFSI, Healthcare, Manufacturing, Retail, Public Sector): Global Opportunity Analysis and Industry Forecast, 2024-2030.

The global Cloud Machine Learning Operations (MLOps) market was valued at US$ 186.4 million in 2023 and is anticipated to reach US$ 3652.7 million by 2030, witnessing a CAGR of 44.6% during the forecast period 2024-2030.

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Major Factors Driving the Growth of Cloud Machine Learning Operations (MLOps) Market:

The growing use of artificial intelligence (AI) and machine learning (ML) across a variety of sectors is fueling the rapid growth of the cloud machine learning operations (MLOps) market. Cloud-based MLOps solutions are increasingly crucial as businesses look to simplify the deployment, monitoring, and management of ML models at scale. These systems help companies speed up time-to-market for AI products, enhance model accuracy, and automate procedures. The market is being driven by the increasing need for affordable and scalable cloud solutions, and major companies are concentrating on improving their MLOps offerings by integrating them with well-known cloud services and cutting-edge analytics tools.

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TRENDS INFLUENCING THE GROWTH OF THE CLOUD MLOPS MARKET:

The use of cloud MLOps (Machine Learning Operations) services is a major growth driver in the BFSI (Banking, Financial Services, and Insurance) industry. Financial institutions are using MLOps more and more to boost fraud detection, handle data more efficiently, and provide individualized client experiences. These companies can effectively manage and implement machine learning models at scale with Cloud MLOps, which improves predictive analytics and decision-making. Cloud-based services enable BFSI businesses to handle enormous volumes of data in real-time, which helps them stay competitive and adaptable to changes in the market. Furthermore, cloud MLOps services’ scalability and flexibility enable easy integration with current IT infrastructure, which accelerates adoption and market expansion.

One of the main factors propelling the growth of the cloud MLOps market is the proliferation of cloud MLOps platforms. These systems offer all-inclusive solutions that optimize every stage of the machine learning process, from model training and data intake to deployment and oversight. Cloud MLOps systems simplify complicated procedures and shorten the time needed to deploy machine learning models into production by providing integrated tools and frameworks. Because of its simplicity of use, businesses from a variety of sectors are drawn to it, which promotes wider adoption. AWS, Google Cloud, and Azure are just a few of the cloud providers that compete fiercely, which encourages ongoing innovation, enhances platform capabilities, and expands the market. Strong cloud MLOps platforms are in greater demand as businesses look for effective methods to use AI and machine learning.

The market for cloud MLOps is expanding thanks in large part to the retail and healthcare industries. Cloud MLOps are utilized in the healthcare industry to improve patient care by means of individualized treatment plans, predictive analytics, and effective health record administration. Real-time data analysis is made possible by cloud-deployed machine learning models, which support early diagnosis and therapy optimization. Cloud MLOps is used in the retail industry for customized marketing, inventory control, and demand forecasting. Real-time consumer data analysis makes it possible for merchants to enhance both customer satisfaction and operational effectiveness. In addition to propelling these industries’ digital transformation, the use of cloud MLOps also greatly expands the industry as a whole.

The exponential development in data creation is one of the main drivers propelling the cloud MLOps industry. Massive volumes of data are being produced every day as a result of the widespread use of digital devices and the Internet of Things (IoT). Systems that are effective are necessary for organizations to handle, examine, and learn from this data. The infrastructure and resources required to manage the deployment of machine learning models and large-scale data processing are provided by Cloud MLOps. Businesses may effectively store, process, and analyze data by utilizing cloud-based solutions. This gives them the ability to make data-driven choices and obtain a competitive advantage. The continual increase in data creation guarantees a consistent need for cloud MLOps platforms and services.

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CLOUD MACHINE LEARNING OPERATIONS (MLOPS) MARKET SHARE

Because of its robust technical infrastructure and the presence of many digital giants like Google, Amazon, and Microsoft, North America is a leader in the cloud MLOps industry. When it comes to creating and implementing cutting-edge cloud MLOps platforms and services, these businesses are in the forefront. The need for cloud MLOps solutions is fueled by the region’s strong rate of adoption of AI and machine learning technology across a variety of industries, including retail, healthcare, and finance. North America’s supremacy in this sector is also a result of significant investments in R&D and a strong startup environment. The region’s market growth is further bolstered by favorable government efforts and regulatory support for data security.

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Key Companies:

IBMDataRobotSASMicrosoftAmazonGOOGLE INCDataikuDatabricksHPELguazioClearMLModzyCometClouderaPaperpaceValohai

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DISCOVER MORE INSIGHTS: EXPLORE SIMILAR REPORTS!

–  MLOps Platform Market

–  Machine Learning Operations (MLOps) Platform Market

–  MLOps Technology Market

–  The Machine Learning Operations (MLOps)market was estimated to be worth US$ 545.5 million in 2023 and is forecast to a readjusted size of US$ 9066.7 million by 2030 with a CAGR of 41.8% during the forecast period 2024-2030.

–  AI & Machine Learning Operationalization (MLOps) Software Market

–  MLOps Solution market was valued at US$ 546.1 million in 2023 and is anticipated to reach US$ 8884.3 million by 2030, witnessing a CAGR of 41.3% during the forecast period 2024-2030.

–  ModelOps and MLOps Platforms Market

–  The global cloud artificial intelligence market was valued at USD 42.7 Billion in 2022, and is projected to reach USD 887 Billion by 2032, growing at a CAGR of 35.8% from 2023 to 2032.

–  Cloud Database Market

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Marquis Who’s Who Honors Rupin Chothani for Engineering Leadership

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UNIONDALE, N.Y., July 23, 2026 /PRNewswire/ — Marquis Who’s Who honors Rupin Chothani for his leadership in engineering and project management. With more than two decades of professional experience to his credit, Mr. Chothani leverages a unique expertise in fire and petrochemical solutions to find success in his field. As project manager, project engineer and proposal manager at Technip Energies N.V., Mr. Chothani ensures effective results.

Drawn to Engineering

Coming from a family of engineers, Mr. Chothani was naturally drawn to the profession. This inclination was reinforced by comprehensive aptitude and attitude tests administered at the age of 14, which highlighted his strengths in engineering and architecture. Ultimately, this direction reinforced his determination to pursue a degree in mechanical engineering.

By 2003, Mr. Chothani earned a Bachelor of Science in Mechanical Engineering at the University of Mumbai. After a brief role as a junior manufacturing engineer at Artech Cooling Tower Pvt. Ltd., he completed a Master of Science in Mechanical Engineering at the University of Bridgeport in 2006. In addition to these degrees, Mr. Chothani later achieved AutoCAD certification.

Following his graduation in 2006, Mr. Chothani joined CB&I Lummus / ABB Lummus Heat Transfer (now Lummus Technology) as a thermal engineer. Though his work at Lummus Technology lasted only three years, Mr. Chothani was greatly influenced by mentor figures at the company. These mentors, including Ken Catala, Peter Harvard, Chin Dang and Miller Alanath Carter, provided essential guidance.

Building a Family

In December 2008, Mr. Chothani married his wife, Cathy. Along with his son and daughter, his family has contributed richly to his success in engineering and they continue to inspire him to excel. In addition to their support, Mr. Chothani recognizes that there is no alternative to hard work and dedicated learning.

From Lummus Technology to Technip Energies N.V.

Following his work at Lummus Technology, Mr. Chothani worked with Maco Corporation India Pvt. Ltd. By 2011, he joined Complete Heat Transfer Solutions – Environ Energy Systems as a thermal and mechanical engineer. By 2013, Mr. Chothani became a part of Technip Energies N.V. as a furnace mechanical engineer. By 2023, he added to this role and became a project manager, project engineer and proposal manager at the company.

In his current role at Technip Energies N.V., Mr. Chothani is responsible for a variety of essential duties. He manages and executes on engineering projects for ethylene cracking furnaces and heaters, and oversees proprietary technologies. Additionally, he actively coordinates with procurement, logistics, mechanical engineering and process engineering teams to ensure effective results.

Plans for the Future

Moving forward, Mr. Chothani hopes to advance his project management skills, particularly within the firejet industry. At the same time, he aims to share his knowledge of the industry with the next generation of professionals. Outside of his professional ambitions, Mr. Chothani intends to prepare his children to find success, inspiring them and their peers with hands-on experiments and full-day events.

About Marquis Who’s Who®:

Since 1899, when A. N. Marquis printed the First Edition of Who’s Who in America®, Marquis Who’s Who® has chronicled the lives of the most accomplished individuals and innovators from every significant field, including politics, business, medicine, law, education, art, religion and entertainment. Who’s Who in America® remains an essential biographical source for thousands of researchers, journalists, librarians and executive search firms worldwide. The suite of Marquis® publications can be viewed at the official Marquis Who’s Who® website, www.marquiswhoswho.com.

Marquis Who’s Who
Uniondale, NY
(844) 394 – 6946
info@marquiswhoswho.com
www.marquiswhoswho.com

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COALITION OF INDEPENDENT INTERNET PROVIDERS ASKS CRTC TO FIX ERRORS IN WHOLESALE FIBRE RATES

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Coalition of competitive ISPs say current fibre rates make competition impossible and threatens to harm millions of Canadian consumers

CHATHAM, ON, July 23, 2026 /CNW/ — A coalition of independent internet service providers (the Coalition) led by TekSavvy Solutions Inc. (TekSavvy) today applied to the Canadian Radio-Television and Telecommunications Commission (CRTC) to review and vary Telecom Order 2026-77, which set final wholesale rates for fibre internet services. In that decision, the CRTC approved wholesale rates for fibre internet services that are higher than the retail prices charged by the large carriers. This makes competition impossible, as independent providers are forced to either sell at a loss or set prices above the large carriers, leaving millions of Canadian consumers without competitive options for essential internet services.

The application identifies key errors that led the CRTC to approve severely inflated final wholesale rates, which make it economically impossible for independent providers to compete. The Coalition argues that the CRTC’s incorrect rates negate the very purpose of Canada’s wholesale framework, which is to foster competition in retail broadband markets. Specifically, the Coalition asks the CRTC to make three key changes to Telecom Order 2026-77:

Eliminate one cost factor that is inconsistent with the CRTC’s established costing principles, which artificially increased fibre wholesale rates by an estimated 25% to 30% (the Adjustment Factor).Reduce another element of the costing that is inflated above reasonable levels: The Coalition calls on the CRTC to reduce the markup applied to wholesale fibre services from 30% to 15%, reflecting declining costs, operational efficiencies, and the need to support competition.Correct technical errors relating to certain wholesale fibre speed descriptions.

“Canadians were promised greater competition for fibre internet services, but these rates make competition impossible.” said Andy Kaplan-Myrth, TekSavvy’s Vice President of Regulatory and Carrier Affairs. “The CRTC must correct these errors to ensure its wholesale rates promote broadband competition that challenges the market power of monopoly incumbents, lowers prices, and increases consumer choice.”

About the Coalition

The Coalition consists of competitive telecommunications providers and industry associations advocating for fair wholesale access to fibre networks and a competitive broadband marketplace that delivers affordable, high-quality Internet services to Canadians, including: TekSavvy Solutions Inc., BC Broadband Association (“BCBA”), Canada-Wide Internet Service Providers Association (“CanWISP”), Fibernetics Inc., ISP Telecom Inc., National Capital FreeNet Inc., Novus Entertainment Inc. and Purple Cow Internet Inc.

About TekSavvy Solution Inc.

Based in Chatham, Ontario, TekSavvy is Canada’s largest independent telecom service company. TekSavvy has been proudly delivering award-winning services and fighting for consumers’ rights for nearly 30 years. TekSavvy is committed to providing quality competitive choice and closing Canada’s digital divide.

SOURCE TekSavvy Solutions Inc.

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Monk Launches Voice Collections, Bringing AI Phone Calls and Callbacks to Accounts Receivable

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Monk’s collections agent, Julia, can now place outbound collection calls and answer inbound AR questions from a dedicated business number, so finance teams can use the channel that collects best without adding headcount.

Multimedia: Watch Voice Collections in action: https://youtu.be/w09PoN1yACE 

NEW YORK, July 23, 2026 /PRNewswire/ — Monk, the AI-native accounts receivable platform, today launched Voice Collections. Its collections agent, Julia, can now place outbound collection calls and answer inbound customer questions about invoices and payments from a dedicated phone number for each organization. The feature brings the phone, long the most effective collections channel and the hardest one to scale, into Monk’s Intelligent Collections.

Roughly $10 trillion sits in unpaid invoices worldwide, and the average invoice now takes 59 days to clear (Allianz). Most accounts receivable runs on email, and most of it waits. More than half of B2B invoices in the United States are overdue at any given time, and 92% of businesses are typically paid after their due date (Chaser, 2026). Phone calls recover overdue invoices two to three times better than email (Dunwise), yet 91% of finance teams still rely on email as their main follow-up channel and only 56% use the phone, because calling every overdue account by hand does not scale and a single human dunning call can cost $12 to $18 (HighRadius).

Voice Collections gives teams that coverage. Julia can call on the accounts a playbook flags for phone follow-up, and answer when a customer calls the same number back to ask about an invoice, a payment, or a bank detail. Businesses that follow up on 100% of overdue invoices are 76% more likely to be paid within a week (Chaser), and a voice agent is what makes full coverage possible.

Monk’s collections agent is already proven on the accounts it handles by email. Across Monk’s first 100 customers, Julia reaches customers with a 24% higher response rate than standard dunning and resolves 88.2% of collections with zero human intervention. Voice extends that reach to the phone.

“For years the assumption was that customers would not talk to an AI on the phone,” said George Kurdin, Founder and CEO of Monk. “The evidence now points the other way. People engage with a good voice agent, and in AR the phone was always the channel that collected best. We built Voice Collections so finance teams can finally use it at the scale email gave them.”

That assumption is worth retiring. In a University of Chicago Booth field study of roughly 70,000 interviews, people interviewed by a voice AI agent were 12% more likely to receive an offer, 18% more likely to start, and 17% more likely to still be there after 30 days, and 80% chose the voice AI over a human when given the choice. The setting was recruiting rather than collections, but the finding travels: given a capable voice agent, people lean in rather than hang up. A call also does something email cannot, which is secure a verbal promise to pay in the moment.

Built for finance, with the phone agents kept with strict guardrails

Voice in finance has to be constrained, and Monk designed Voice Collections around that from the start. The agent is read-only on the phone. It answers questions, confirms details, and routes the next step. It will not rewrite an invoice, change a payment status, or accept a sensitive payment change by voice.

The agent is also reference-based. If a caller asks about an invoice, Julia asks for both the company name and the invoice number before looking anything up, and it will not search broadly from a single detail. Every inbound and outbound call is kept in the collection record alongside the email history, so a callback is part of the same thread the team already sees, and anything that needs judgment escalates to a person.

“Voice in finance has to be careful by design,” said Joe Zhou, Co-Founder and CTO of Monk. “Julia will not browse across accounts or move money over the phone. A caller has to bring the company name and invoice number before it confirms anything, and every call lands in the record. In finance a 1% mistake is still unacceptable, so we built for that first and added the reach second.”

Teams run autonomous collections on Monk

Monk runs collections for finance teams at companies like Unify, Pump, Siro, and Elate, and Voice Collections extends what those teams already do by email onto the phone.

“We chose Monk to help automate our collections, a process previously demanding several hours a week of manual, one-off outreach,” said Will Stewart, Head of Finance and BizOps at Unify. “Today, our Monk agent is always running in the background and I have a single dashboard to manage AR from.”

At Pump, which manages volume across more than 1,500 customers, Monk has helped collect over $10 million in recent months.

Voice AI is now infrastructure

The timing reflects how far voice AI has come. It has moved from demo to infrastructure: Vapi has processed more than 1 billion calls, Bland handles over 3.5 million calls a week, and ElevenLabs raised a $500 million round at an $11 billion valuation in early 2026. Monk builds Voice Collections on that foundation and adds the part finance actually needs, which is the AR context, the controls, and the audit trail.

Voice Collections is available now as an opt-in feature. Monk configures the dedicated number and call behavior with each organization before turning it on in Collections. See it in action: https://youtu.be/w09PoN1yACE.

About Monk

Monk is the AI-native accounts receivable platform that helps finance teams turn revenue into cash. Its agent, Julia, runs collections, cash application, and forecasting as one connected system. Monk resolves 88.2% of collections with zero human intervention, reaches customers with a 24% higher response rate than standard dunning, reduces DSO by more than 40%, automatically matches 80% of incoming payments with a full audit trail, and gives finance teams back roughly 26 hours a month. Teams onboard in under a week and see results in their first month. More than $1.5 billion in receivables is managed on the platform, including for customers like Profound and ElevenLabs. Monk has raised $25 million and is based in New York.

Media contact
Kendall Warson
kendall@monk.com
+1 415-827-6585

Sources: Chaser 2026 Accounts Receivable research; Dunwise dunning research; HighRadius collection call cost analysis; University of Chicago Booth field study on AI in recruiting; voice AI figures compiled by Enterprise DNA; Federal Reserve data; Allianz Worldwide DSO survey.

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