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This report titled as Global Machine Learning Operationalization Software Market, gives a brief about the comprehensive research and an outline of its growth in the market globally. It states about the significant market drivers, trends, limitations and opportunities to give a wide-ranging and precise data and also scrutinizes its growth in the overall markets development which is needed and expected.

The report also summarizes the various types of the Global Machine Learning Operationalization Software Market. Factors that influence the market growth of particular product category type and market status for it. A detailed study of the Global Machine Learning Operationalization Software Market has been done to understand the various applications of the products usage and features. Readers looking for scope of growth with respect to product categories can get all the desired information over here, along with supporting figures and facts.

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Market Segment as follows:

Global Machine Learning Operationalization Software: Regional Segment Analysis

North America

Europe

Asia Pacific

Middle East & Africa

South America

Companies Profiled in this report includes.

MathWorks

SAS

Microsoft

ParallelM

Algorithmia

H20.ai

TIBCO Software

SAP

IBM

Domino

Seldon

Datmo

Actico

RapidMiner

KNIME

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Key questions answered in the report include:

What will the market size and the growth rate be in 2026?

What are the key factors driving the Global Machine Learning Operationalization Software Market?

What are the key market trends impacting the growth of the global Machine Learning Operationalization Software Market?

What are the challenges to market growth?

Who are the key vendors in the Global Machine Learning Operationalization Software Market?

What are the market opportunities and threats faced by the vendors in the global Machine Learning Operationalization Software Market?

What are the trending factors influencing the market shares of the Americas, APAC, Europe, and MEA?

What are the key outcomes of the five forces analysis of the Global Machine Learning Operationalization Software Market?

This report provides pinpoint analysis for changing competitive dynamics. It offers a forward-looking perspective on different factors driving or limiting market growth. It provides a five-year forecast assessed on the basis of how the Global Machine Learning Operationalization Software Market is predicted to grow. It helps in understanding the key product segments and their future and helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments.

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Comprehensive Analysis of Global Machine Learning Operationalization Software Market with Current and Future Business Outlook | MathWorks, SAS,...

At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. Its a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesnt traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.

The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. Machine learning in the enterprise is in crisis, in my view, Craig Wiley, the director of product management for Google Clouds AI Platform, told me. As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.

Image Credits: Google

Wiley, who was also the general manager of AWSs SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, many of which were dead ends, according to him (including some of Googles own). Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models theyre building.

Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.

Image Credits: Google

The service is also integrated with Vizier, Googles AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.

Vertex also offers a Feature Store that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.

Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Clouds AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.

To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and and this may be a bit of a surprise BigQuery ML, Googles tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.

We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production, said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.

Continue reading here:
Google Cloud launches Vertex AI, a new managed machine learning platform - TechCrunch

Media and publishing used to be the domain of specialized companies who controlled the content. The internet broke that model, and today anyone can go online and publish a blog, a podcast, or star in their own video.

But the big tech companies want to take control, closing content into walled gardens. But thats not what the majority of publishers, big or small, want.

We get to hear the perspectives of the publishers at every scale, and they consistently tell us the same thing: They want to more directly connect to consumers; they dont want to be tied into these walled gardens which dictate how they must present their content and in some cases what content theyre allowed to present, said Dr. Peter Day (pictured, right), chief technology officer at Quantcast Corp.

Day and Shruti Koparkar (pictured, left), head of product marketing at Quantcast, spoke with John Furrier, host of theCUBE, SiliconANGLE Medias livestreaming studio, duringThe Cookie Conundrum: A Recipe for Success event. They discussed the importance of smart technology for the post-cookie future of digital marketing. (* Disclosure below.)

Quantcast has cast itself as a champion of the open internet as it sets out to find the middle ground between the ability to scale provided by walled gardens and access to individual-level user data. Urgency for the quest is provided by Goliath company Google, which announced it will no longer be supporting third-party cookies on its Chrome browser as of January 2022.

Our approach to a world without third-party cookies is grounded in three fundamental things, Koparkar stated. First is industry standards: We think its really important to participate and to work with organizations who are defining the standards that will guide the future of advertising, Koparkar said, naming IAB Technology Laboratorys Project Rearc and Prebid as open projects Quantcast is involved with.

The companys engineering team also participates in meetings with the World Wide Web Consortium (W3C) to keep on top of what is happening with web browsers and to monitor what Google is up to with its Federated Learning of Cohorts (FLoC) project.

The second fundamental principle to Quantcasts strategy is interoperability. With multiple identity solutions from Unified ID 2.0 to FLoC already existing, and more on the way, We think it is important to build a platform that can ingest all of these signals, and so thats what weve done, Koparkar said referring to the release of Quantcasts intelligent audience platform.

Innovation is the third principle. Being able to take in multiple signals, not only IDs and cohorts, but also contextual first-party consent, time, language, geolocation and many others is increasingly important, according to Kopackar.

All of these signals can help us understand user behavior, intent and interests in absence of third-party cookies, she said.

But these signals are raw, messy, complex and ever-changing. What you need is technology like AI and machine learning to bring all of these signals together, combine them statistically, and get an understanding of user behavior, intent and interest, and then act on it, Koparkar stated. And the only way to bring them all together to obtain coherent understanding is through intelligent technologies such as machine learning, she added.

The foundation of our platform has always been machine learning from before it was cool, Day said. Many of the core team members at Quantcast have doctorate degrees in statistics and ML, which means it drives the companys decision-making.

Data is only useful if you can make sense of it, if you can organize it, and if you can take action on it, Day said. And to do that at this kind of scale its absolutely necessary to use machine learning technology.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of The Cookie Conundrum: A Recipe for Success event. (* Disclosure: TheCUBE is a paid media partner for The Cookie Conundrum: A Recipe for Success event. Neither Quantcast Corp., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Wed also like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we dont have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary onSiliconANGLE along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams attheCUBE take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

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Quantcast uses machine learning and AI to take on walled garden giants in the fight for the open internet - SiliconANGLE News

A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. Credit: Los Alamos National Laboratory

A black hole permanently scrambles information that cant be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment.

A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling.

Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes, said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters.

Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results dont condemn quantum machine learning, but rather highlight the importance of understanding its limits, Holmes said.

In the classic Hayden-Preskill thought experiment, a fictitious Alice tosses information such as a book into a black hole that scrambles the text. Her companion, Bob, can still retrieve it using entanglement, a unique feature of quantum physics. However, the new work proves that fundamental constraints on Bobs ability to learn the particulars of a given black holes physics means that reconstructing the information in the book is going to be very difficult or even impossible.

Any information run through an information scrambler such as a black hole will reach a point where the machine learning algorithm stalls out on a barren plateau and thus becomes untrainable. That means the algorithm cant learn scrambling processes, said Andrew Sornborger a computer scientist at Los Alamos and coauthor of the paper. Sornborger is Director of Quantum Science Center at Los Alamos and leader of the Centers algorithms and simulation thrust. The Center is a multi-institutional collaboration led by Oak Ridge National Laboratory.

Barren plateaus are regions in the mathematical space of optimization algorithms where the ability to solve the problem becomes exponentially harder as the size of the system being studied increases. This phenomenon, which severely limits the trainability of large scale quantum neural networks, was described in a recent paper by a related Los Alamos team.

Recent work has identified the potential for quantum machine learning to be a formidable tool in our attempts to understand complex systems, said Andreas Albrecht, a co-author of the research. Albrecht is Director of the Center for Quantum Mathematics and Physics (QMAP) and Distinguished Professor, Department of Physics and Astronomy, at UC Davis. Our work points out fundamental considerations that limit the capabilities of this tool.

In the Hayden-Preskill thought experiment, Alice attempts to destroy a secret, encoded in a quantum state, by throwing it into natures fastest scrambler, a black hole. Bob and Alice are the fictitious quantum dynamic duo typically used by physicists to represent agents in a thought experiment.

You might think that this would make Alices secret pretty safe, Holmes said, but Hayden and Preskill argued that if Bob knows the unitary dynamics implemented by the black hole, and share a maximally entangled state with the black hole, it is possible to decode Alices secret by collecting a few additional photons emitted from the black hole. But this prompts the question, how could Bob learn the dynamics implemented by the black hole? Well, not by using quantum machine learning, according to our findings.

A key piece of the new theorem developed by Holmes and her coauthors assumes no prior knowledge of the quantum scrambler, a situation unlikely to occur in real-world science.

Our work draws attention to the tremendous leverage even small amounts of prior information may play in our ability to extract information from complex systems and potentially reduce the power of our theorem, Albrecht said. Our ability to do this can vary greatly among different situations (as we scan from theoretical consideration of black holes to concrete situations controlled by humans here on earth). Future research is likely to turn up interesting examples, both of situations where our theorem remains fully in force, and others where it can be evaded.

Reference: Barren Plateaus Preclude Learning Scramblers by Zo Holmes, Andrew Arrasmith, Bin Yan, Patrick J. Coles, Andreas Albrecht and Andrew T. Sornborger, 12 May 2021, Physical Review Letters.DOI: 10.1103/PhysRevLett.126.190501

Funding: U.S. Department of Energy, Office of Science

Read more from the original source:
Quantum Machine Learning Hits a Limit: A Black Hole Permanently Scrambles Information That Can't Be Recovered - SciTechDaily

Artificial intelligence (AI) is more accessible than ever and is increasingly used to improve business operations and outcomes, not only in transportation and logistics management, but also in diverse fields like finance, healthcare, retail and others. An Oxford Economics and NTT DATA survey of 1,000 business leaders conducted in early 2020 reveals that 96% of companies were at least researching AI solutions, and over 70% had either fully implemented or at least piloted the technology.

Nearly half of survey respondents said failure to implement AI would cause them to lose customers, with 44% reporting their companys bottom line would suffer without it.

Simply put, AI enables companies to parse vast quantities of business data to make well-informed and critical business decisions fast. And, the transportation management industry specifically is using this intelligence and its companion technology, machine learning (ML), to gain greater process efficiency and performance visibility driving impactful changes bolstering the bottom line.

McKinsey research reveals that 61% of executives report decreased costs and 53% report increased revenues as a direct result of introducing AI into their supply chains. For supply chains, lower inventory-carrying costs, inventory reductions and lower transportation and labor costs are some of the biggest areas for savings captured by high volume shippers. Further, AI boost supply chain management revenue in sales, forecasting, spend analytics and logistics network optimization.

For the trucking industry and other freight carriers, AI is being effectively applied to transportation management practices to help reduce the amount of unprofitable empty miles or deadhead trips a carrier makes returning to domicile with an empty trailer after delivering a load. AI also identifies other hidden patterns in historical transportation data to determine the optimal mode selection for freight, most efficient labor resource planning, truck loading and stop sequences, rate rationalization and other process improvement by applying historical usage data to derive better planning and execution outcomes.

The ML portion of this emerging technology helps organizations optimize routing and even plan for weather-driven disruptions. Through pattern recognition, for instance, ML helps transportation management professionals understand how weather patterns affected the time it took to carry loads in the past, then considers current data sets to make predictive recommendations.

The Coronavirus disease (COVID-19) put a tremendous amount of pressure on many industries the transportation industry included but it also presented a silver lining -- the opportunity for change. Since organizations are increasingly pressed to work smarter to fulfill customers expectations and needs, there is increased appetite to retire inefficient legacy tools and invest in new processes and tech tools to work more efficiently.

Applying AI and ML to pandemic-posed challenges can be the critical difference between accelerating or slowing growth for transportation management professionals. When applied correctly, these technologies improve logistics visibility, offer data-driven planning insights and help successfully increase process automation.

Like many emerging technologies promising transformation, AI and ML have, in many cases, been misrepresented or worse, overhyped as panaceas for vexing industry challenges. Transportation logistics organizations should be prudent and perform due diligence when considering when and how to introduce AI and ML to their operations. Panicked hiring of data scientists to implement expensive, complicated tools and overengineered processes can be a costly boondoggle and can sour the perception of the viability of these truly powerful and useful tech tools. Instead, organizations should invest time in learning more about the technology and how it is already driving value for successful adopters in the transportation logistics industry. What are some steps a logistics operation should take as they embark on an AI/ML initiative?

Remember that the quality of your data will drive how fast or slow your AI journey will go. The lifeblood of an effective AI program (or any big data project) is proper data hygiene and management. Unfortunately, compiling, organizing and accessing this data is a major barrier for many. According to a survey conducted by OReilly, 70% of respondents report that poorly labeled data and unlabeled data are a significant challenge. Other common data quality issues respondents cited include poor data quality from third-party sources (~42%), disorganized data stores and lack of metadata (~50%) and unstructured, difficult-to-organize data (~44%).

Historically slow-to-adopt technology, the transportation industry has recently begun realizing the imperative and making up ground with 60% of an MHI and Deloitte poll respondents expecting to embrace AI in the next five years. Gartner predicts that by the end of 2024, 75% of organizations will move from piloting to operationalizing AI, driving a five times increase in streaming data and analytics infrastructures.

For many transportation management companies, accessing, cleansing and integrating the right data to maximize AI will be the first step. AI requires large volumes of detailed data and varied data sources to effectively identify models and develop learned behavior.

Before jumping on the AI bandwagon too quickly, companies should assess the quality of their data and current tech stacks to determine what intelligence capabilities are already embedded.

And, when it comes to investing in newer technologies to pave the path toward digital transformation, choose AI-driven solutions that do not require you to become a data scientist.

If youre unsure how to start, consider partnering with a transportation management system (TMS) partner with a record of experience and expertise in applying AI to transportation logistics operations.

Read more:
Artificial Intelligence and Machine Learning Drive the Future of Supply Chain Logistics - Supply and Demand Chain Executive