Archive for the ‘Machine Learning’ Category

Artificial Intelligence & Advanced Machine Learning Market Global Sales, Revenue, Price and Gross Margin Forecast To 2030 | Next IT Corp., Prisma…

Allied Market Research recently published a report titled, Artificial Intelligence & Advanced Machine Learning Market in BFSI by solution (Smart Wallets and Voice Assisted Banking) and End User (Insurance, Banking, and Capital Markets) Global Opportunity Analysis and Industry Forecast, 2021-2028.

The artificial intelligence & advanced machine learning market report presents the top factors and market trends that are real behind the growth of the market. It also offers an extensive study of evolving market dynamics, top investment pockets, key sections, competitive scenarios, value chain, and major investment prospects. These figures and data are helpful for startups, stakeholders, investors, and the key market players to obtain useful understandings and visions on the market and implement necessary strategies based on the same.

Thorough analysis of each section and sub-section is offered in the research with the help of horizontal and graphical depiction. This breakdown is helpful in assessing the fastest growing segments and establishing strategies to attain sustainable growth. The report has offered an all-inclusive analysis of the global artificial intelligence & advanced machine learning market taking into consideration all the crucial aspects like growth factors, constraints, market developments, top investment pockets, future prospects, and trends. At the start, the report lays emphasis on the key trends and opportunities that may emerge in the near future and positively impact the overall industry growth.

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Key Market players in the Artificial intelligence & advanced machine learning Market:

The artificial intelligence & advanced machine learning market report doles out the reference of the major player in the market including iCarbonX, Jibo. Inc, Next IT Corp., Prisma Labs, Inc., AIBrain, Quadratic Insights Pvt. Ltd., NVIDIA Corporation, Inbenta Technologies Inc., Numenta, and Intel Corporation.. These players have taken recourse to essential strategies including alliance, collaboration, development, joint ventures, and others to heighten their stance in the industry.

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The study on the global artificial intelligence & advanced machine learning market incorporates 200+ pages report that offers noteworthy information along with underscoring the drivers, restraints, and opportunities of the market. The study also aims to proffer a comprehensive information on the recent market leanings, movements, approaches, and stratagems espoused by the market players in the global artificial intelligence & advanced machine learning market. The report encompasses both the projected as well as historical data throughout the forecast period along with other facets including product outline and growth opportunities.

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By Solution Smart Wallets Voice-Assisted Banking

By End-user Insurance Banking and Capital Markets

Artificial intelligence & advanced machine learning Market Geographical Analysis: The report also provides a regional analysis of the market along with the competitive setting in each region. The research report encompasses regions including North America (the United States, Canada, and Mexico), Europe (Germany, France, UK, Russia, and Italy), Asia-Pacific (China, Japan, Korea, India, and Southeast Asia), South America (Brazil, Argentina, and Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa). These segmentations aid the market players in framing respective tactics, thus leading way to lucrative opportunities to attain their objectives.

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Artificial Intelligence & Advanced Machine Learning Market Global Sales, Revenue, Price and Gross Margin Forecast To 2030 | Next IT Corp., Prisma...

Provectus Announces a New Partnership with Tecton to Collaborate on Feature Store for Machine Learning – IT News Online

PR.com2021-05-20

Palo Alto, CA, May 20, 2021 --(PR.com)-- Provectus, an AI-first consultancy and solutions provider, today announced its partnership with Tecton, the enterprise feature store company, with the aim of standardizing and enhancing the machine learning production stack for enterprises.

Provectus and Tecton will closely collaborate in three major areas:

Joint contributions and enhancement of Feast. Provectus will contribute to the next generation of Feast, the leading open-source feature store. While Tecton will remain the major contributor to Feast, Provectuss efforts will be focused on bringing Feast to AWS as well as on core data models and APIs.

Provectus - a partner of choice. Provectus will become a partner of choice for Tecton, providing users of both the Tecton and Feast feature stores with consulting and professional services.

Design and standardization of open-source feature store APIs. Provectus and Tecton, along with users of ML feature stores, will join forces to define common, non-opinionated vendor agnostic APIs for feature stores.

Feature Stores for Machine Learning is a relatively new concept. They constitute the most critical piece in the modern ML production stack. As such, feature stores need to be clearly defined, standardized, and aligned with industry best practices. Provectus and Tectonhave the experience and expertise to accomplish those goals.

When we introduce a feature store to our clients, the platform acts as an essential component that provides a clear path to production for online ML use cases such as Recommendations and Fraud Detection. You will have to build it one way or another, says Stepan Pushkarev, CTO at Provectus.

Tectons founding team previously built the Michelangelo feature store at Uber. Willem Pienaar, who now serves as tech lead at Tecton, pioneered the development of Feast, one of the first open source feature stores. Provectus has deep expertise designing and building feature stores for various enterprise clients.

Strategy-wise, composability, cloud agnosticism, and a multi-cloud approach are fundamental guidelines for the CIOs of modern enterprises. The fact is, machine learning infrastructure is not an exception, says Pushkarev. For Provectus, collaboration with Tecton and Feast is a chance to unite the community towards a better machine learning stack for enterprises.

In the past few years, feature stores have evolved considerably; Provectus, Feast, and Tecton share a common vision of their future, which is outlined in the recent blog posts A State of Feast and Feature Store as a Foundation for ML. A new version of Feast has recently emerged and was announced by Willem Pienaar & Jay Parthasarthy. At the apply() conference, the Provectus team gave a presentation on the roadmap for Feast and AWS integration.

About ProvectusProvectus is an Artificial Intelligence consultancy and solutions provider, helping companies in Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses achieve their objectives through AI. Provectus is headquartered in Palo Alto, CA. For more information, visit provectus.com

About TectonTecton provides an enterprise-ready feature store for machine learning that enables organizations to manage the complete lifecycle of features, from engineering new features to serving them in production for real-time predictions. Tecton is headquartered in San Francisco, CA. For more information, visit tecton.ai

About FeastFeast is an open source feature store for machine learning that helps data scientists and ML engineers bridge the gap between data and machine learning models. It provides the fastest path to production for ML features. For more information, visit feast.dev

Contact Information:ProvectusIryna Ryslyayeva+1-800-950-9840Contact via Emailhttps://provectus.com/

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Global Machine Learning Market To Power Robustly And To Witness Profitable Growth During The Forecast Period 2020-2026 The Manomet Current – The…

The business report released by Zion Market Research onGlobal Machine Learning Market To Power Robustly And To Witness Profitable Growth During The Forecast Period 2020-2026is focused to facilitate a deep understanding of the market definition, potential, and scope. The report is curate after deep research and analysis by experts. It consists of an organized and methodical explanation of current market trends to assist the users to entail in-depth market analysis. The report encompasses a comprehensive assessment of different strategies like mergers & acquisitions, product developments, and research & developments adopted by prominent market leaders to stay at the forefront in the global market.

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The major players in the globalMachine Learning MarketareInternational Business Machines Corporation, Microsoft Corporation, Amazon Web ServicesInc., BigmlInc., Google Inc., Hewlett Packard Enterprise Development Lp, Intel Corporation, and others.

Along with contributing significant value to the users, the report by Zion Market Research has focused on Porters Five Forces analysis to put forward the wide scope of the market in terms of opportunities, threats, and challenges. The information extracted through different business models like SWOT and PESTEL is represented in the form of pie charts, diagrams, and other pictorial representations for a better and faster understanding of facts. The report can be divided into following main parts.

Growth drivers:

The report provides an accurate and professional study of global Machine Learning Market business scenarios. The complex analysis of opportunities, growth drivers, and the future forecast is presented in simple and easily understandable formats. The report comprehends the Machine Learning Market by elaborating the technology dynamics, financial position, growth strategy, product portfolio during the forecast period.

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Segmentation:

The report is curate on the basis of segmentation and sub-segmentation that are aggregated from primary and secondary research. Segmentation and sub-segmentation is a consolidation of industry segment, type segment, channel segment, and many more. Further, the report is expanded to provide you thorough insights on each segment.

Regional analysis:

The report covers all the regions in the world showing regional developmental status, the market volume, size, and value. It facilitates users valuable regional insights that will provide a complete competitive landscape of the regional market. Further, different regional markets along with their size and value are illustrated thoroughly in the report for precise insights.

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Competitive analysis:

The report is curate after a SWOT analysis of major market leaders. It contains detailed and strategic inputs from global leaders to help users understand the strength and weaknesses of the key leaders. Expert analysts in the field are following players who are profiled as prominent leaders in the Machine Learning Market. The report also contains the competitive strategy adopted by these market leaders to the market value. Their research and development process was explained well enough by experts in the global Machine Learning Market to help users understand their working process.

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Cardstream partners with Kount for fraud prevention – The Paypers

US-based Cardstream has announced that it is partnering with Kount to deliver AI-driven integrated fraud protection solutions to its OpenPayment Network.

Kount offers different fraud prevention solutions, including the Partner Central Solution, which is a platform designed to protect payment service providers and their merchants. Kounts AI-Driven Fraud Protection model utilises two types of advanced Machine Learning (ML) AI to detect fraudulent patterns successfully. Based on its own data analysis learning, Kounts two types of ML Models are:

Supervised Machine-Learning Model using predetermined data such as a digital fraud profile that the model can quickly look at to identify incoming the fraudulent data;

Unsupervised Machine-Learning Model this a system that relies solely on making its own interpretations from the data by detecting patterns and anomalies.

Kounts Omniscore scoring feature, a transaction safety rating, combines analytical elements of both supervised and unsupervised machine learning into one score. Using its universal data network comprising billions of historical and current transactions, Kount has created machine learning algorithms that any digital financial business can use.

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What Is Machine Learning? | Definition, Types, and …

Machine learning is a subset of artificial intelligence (AI). It is focused on teaching computers to learn from data and to improve with experience instead of being explicitly programmed to do so. In machine learning, algorithms are trained to find patterns and correlations in large datasets and to make the best decisions and predictions based on that analysis. Machine learning applications improve with use and become more accurate the more data they have access to. Applications of machine learning are all around us in our homes, our shopping carts, our entertainment media, and our healthcare.

Machine learning and its components of deep learning and neural networks all fit as concentric subsets of AI. AI processes data to make decisions and predictions. Machine learning algorithms allow AI to not only process that data, but to use it to learn and get smarter, without needing any additional programming. Artificial intelligence is the parent of all the machine learning subsets beneath it. Within the first subset is machine learning; within that is deep learning, and then neural networks within that.

An artificial neural network (ANN) is modeled on the neurons in a biological brain. Artificial neurons are called nodes and are clustered together in multiple layers, operating in parallel. When an artificial neuron receives a numerical signal, it processes it and signals the other neurons connected to it. As in a human brain, neural reinforcement results in improved pattern recognition, expertise, and overall learning.

This kind of machine learning is called deep because it includes many layers of the neural network and massive volumes of complex and disparate data. To achieve deep learning, the system engages with multiple layers in the network, extracting increasingly higher-level outputs. For example, a deep learning system that is processing nature images and looking for Gloriosa daisies will at the first layer recognize a plant. As it moves through the neural layers, it will then identify a flower, then a daisy, and finally a Gloriosa daisy. Examples of deep learning applications include speech recognition, image classification, and pharmaceutical analysis.

Machine learning is comprised of different types of machine learning models, using various algorithmic techniques. Depending upon the nature of the data and the desired outcome, one of four learning models can be used: supervised, unsupervised, semi-supervised, or reinforcement. Within each of those models, one or more algorithmic techniques may be applied relative to the datasets in use and the intended results. Machine learning algorithms are basically designed to classify things, find patterns, predict outcomes, and make informed decisions.Algorithms can be used one at a time or combined to achieve the best possible accuracy when complex and more unpredictable data is involved.

Supervised learning is the first of four machine learning models. In supervised learning algorithms, the machine is taught by example. Supervised learning models consist of input and output data pairs, where the output is labeled with the desired value. For example, lets say the goal is for the machine to tell the difference between daisies and pansies. One binary input data pair includes both an image of a daisy and an image of a pansy. The desired outcome for that particular pair is to pick the daisy, so it will be pre-identified as the correct outcome.

By way of an algorithm, the system compiles all of this training data over time and begins to determine correlative similarities, differences, and other points of logic until it can predict the answers for daisy-or-pansy questions all by itself. It is the equivalent of giving a child a set of problems with an answer key, then asking them to show their work and explain their logic. Supervised learning models are used in many of the applications we interact with every day, such as recommendation engines for products and traffic analysis apps like Waze, which predict the fastest route at different times of day.

Unsupervised learning is the second of the four machine learning models. In unsupervised learning models, there is no answer key. The machine studies the input data much of which is unlabeled and unstructured and begins to identify patterns and correlations, using all the relevant, accessible data. In many ways, unsupervised learning is modeled on how humans observe the world. We use intuition and experience to group things together. As we experience more and more examples of something, our ability to categorize and identify it becomes increasingly accurate. For machines, experience is defined by the amount of data that is input and made available. Common examples of unsupervised learning applications include facial recognition, gene sequence analysis, market research, and cybersecurity.

Semi-supervised learning is the third of four machine learning models. In a perfect world, all data would be structured and labeled before being input into a system. But since that is obviously not feasible, semi-supervised learning becomes a workable solution when vast amounts of raw, unstructured data are present. This model consists of inputting small amounts of labeled data to augment unlabeled datasets. Essentially, the labeled data acts to give a running start to the system and can considerably improve learning speed and accuracy. A semi-supervised learning algorithm instructs the machine to analyze the labeled data for correlative properties that could be applied to the unlabeled data.

As explored in depth in this MIT Press research paper, there are, however, risks associated with this model, where flaws in the labeled data get learned and replicated by the system. Companies that most successfully use semi-supervised learning ensure that best practice protocols are in place. Semi-supervised learning is used in speech and linguistic analysis, complex medical research such as protein categorization, and high-level fraud detection.

Reinforcement learning is the fourth machine learning model. In supervised learning, the machine is given the answer key and learns by finding correlations among all the correct outcomes. The reinforcement learning model does not include an answer key but, rather, inputs a set of allowable actions, rules, and potential end states. When the desired goal of the algorithm is fixed or binary, machines can learn by example. But in cases where the desired outcome is mutable, the system must learn by experience and reward. In reinforcement learning models, the reward is numerical and is programmed into the algorithm as something the system seeks to collect.

In many ways, this model is analogous to teaching someone how to play chess. Certainly, it would be impossible to try to show them every potential move. Instead, you explain the rules and they build up their skill through practice. Rewards come in the form of not only winning the game, but also acquiring the opponents pieces. Applications of reinforcement learning include automated price bidding for buyers of online advertising, computer game development, and high-stakes stock market trading.

Machine learning algorithms recognize patterns and correlations, which means they are very good at analyzing their own ROI. For companies that invest in machine learning technologies, this feature allows for an almost immediate assessment of operational impact. Below is just a small sample of some of the growing areas of enterprise machine learning applications.

See SAP intelligent technologies including AI and machine learning in action

In his book Spurious Correlations, data scientist and Harvard graduate Tyler Vigan points out that Not all correlations are indicative of an underlying causal connection. To illustrate this, he includes a chart showing an apparently strong correlation between margarine consumption and the divorce rate in the state of Maine. Of course, this chart is intended to make a humorous point. However, on a more serious note, machine learning applications are vulnerable to both human and algorithmic bias and error. And due to their propensity to learn and adapt, errors and spurious correlations can quickly propagate and pollute outcomes across the neural network.

The SAP AI Ethics Steering Committee has created guidelines to steer the development and deployment of our AI software.

An additional challenge comes from machine learning models, where the algorithm and its output are so complex that they cannot be explained or understood by humans. This is called a black box model and it puts companies at risk when they find themselves unable to determine how and why an algorithm arrived at a particular conclusion or decision.

Fortunately, as the complexity of datasets and machine learning algorithms increases, so do the tools and resources available to manage risk. The best companies are working to eliminate error and bias by establishing robust and up-to-date AI governance guidelines and best practice protocols.

Machine learning is a subset of AI and cannot exist without it.AI uses and processes data to make decisions and predictions it is the brain of a computer-based system and is the intelligence exhibited by machines. Machine learning algorithms within the AI, as well as other AI-powered apps, allow the system to not only process that data, but to use it to execute tasks, make predictions, learn, and get smarter, without needing any additional programming. They give the AI something goal-oriented to do with all that intelligence and data.

Yes, but it should be approached as a business-wide endeavor, not just an IT upgrade.The companies that have the best results with digital transformation projects take an unflinching assessment of their existing resources and skill sets and ensure they have the right foundational systems in place before getting started.

Relative to machine learning, data science is a subset; it focuses on statistics and algorithms, uses regression and classification techniques, and interprets and communicates results. Machine learning focuses on programming, automation, scaling, and incorporating and warehousing results.

Machine learning looks at patterns and correlations; it learns from them and optimizes itself as it goes.Data mining is used as an information source for machine learning.Data mining techniques employ complex algorithms themselves and can help to provide better organized datasets for the machine learning application to use.

The connected neurons with an artificial neural network are called nodes, which are connected and clustered in layers.When a node receives a numerical signal, it then signals other relevant neurons, which operate in parallel.Deep learning uses the neural network and is deep because it uses very large volumes of data and engages with multiple layers in the neural network simultaneously.

Machine learning is the amalgam of several learning models, techniques, and technologies, which may include statistics.Statistics itself focuses on using data to make predictions and create models for analysis.

Follow in the footsteps of fast learners with these five lessons learned from companies that achieved success with machine learning.

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