Category Archives: Machine Learning
AI and Advance Machine Learning in BFSI Market Global Report 2021-2030 Featuring Leading Players – Cisco, SAP, Microsoft and IBM Among Others -…
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AI and Advance Machine Learning in BFSI Market Global Report 2021-2030 Featuring Leading Players - Cisco, SAP, Microsoft and IBM Among Others -...
Top Python Machine Learning Libraries to Explore in 2022 – Analytics Insight
These 10 python machine learning libraries are the best
Python is the most popular programming language for data science projects. And on the other side, machine learning is a trending topic that is across the globe these days. Python machine learning libraries have become the language for implementing machine learning algorithms. To grasp data science and machine learning, you need to learn Python. Here are the top Python machine learning libraries to explore in 2022.
TensorFlow is an open-source numerical computing library for machine learning based on neural networks. It was created by the Google Brain research team in 2015 to use internally in Google products. Later, it started to gain a lot of popularity among many companies and start-ups such as Airbnb, PayPal, Airbus, Twitter, and VSCO using it on their technology stacks. It is one of the top Python machine learning libraries to explore.
PyTorch is one of the largest machine learning libraries that was designed and developed by Facebooks AI research group. It is used for natural language processing, computer vision, and other similar kinds of tasks. It is one of the top python machine learning libraries to explore. It is used by companies such as Microsoft, Facebook, Walmart, Uber, and others.
Keras is a fast experimentation platform with deep neural networks but it has soon gained a standalone Python ML library. It has a comprehensive ML toolset that aids companies such as Square, Yelp, Uber, and others to handle text and image data effectively. It has a user-friendly interface and has multi-backend support. It has a modular and extensible architecture. It is one of the top Python machine learning libraries to explore.
Orage3 is a software package that includes tools for machine learning, data mining, and data visualization. It was developed in 1996, the scientists at the University of Ljubljana created it with C++. It is one of the top Python machine learning libraries to explore. The features that make Orange3 qualify for this top list are powerful prediction modeling and algorithm testing, widget-based structure, and ease of learning.
Python wasnt initially developed as a tool for numerical computing. The advent of NumPy was the key to expanding Pythons abilities as mathematical functions, based on which machine learning solutions would be built. Using this library is beneficial because of robust computing capabilities, the large programming community, and high performance. It is one of the top Python machine learning libraries to explore.
Along with NumPy, this library is a core tool for accomplishing mathematical, engineering computations, and scientific. The main reasons why Python specialists appreciate SciPy are its easy-to-use library, fast computational power, and improved computations. SciPy is built on top of NumPy and can operate on its arrays, ensuring higher quality and faster execution of computing operations. It is one of the top python machine learning libraries to explore.
Scikit-learn was firstly made as a third-party extension to the SciPy library. It is one of the top libraries on GitHub. The library is an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others. It is one of the top python machine learning libraries to explore. Scikit-learn also found a place on our list because it is great at classical machine learning algorithms, easily interoperable with other SciPy stack tools.
Pandas is a low-level Python library built upon NumPy. Everything started with the AQR financial company that needed help with quantitative analysis of its financial data. Wes McKinney is a developer at the company who started the creation of Pandas. Pandas have powerful data frames and flexible data handling. It is one of the top Python machine learning libraries to explore.
A unity of NumPy, Matplotlib, and SciPy is supposed to replace the need to use the proprietary MATLAB statistical language. Python packages are also available for free and more flexibly which can make a choice of many data scientists. It is one of the top Python machine learning libraries to explore. The reason to include Matplotlib is because of its comprehensive set of plotting tools.
In 2007, the Montreal Institute of Learning Algorithms was created by Theano for evaluating and manipulating various mathematical expressions. Based on these expressions, the Python machine learning library allows building optimized deep learning neural networks. It has a stable simultaneous computing, fast execution speed, and optimized stability. It is one of the top python machine learning libraries to explore.
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Chat Commerce, machine learning and a stronger privacy focus eCommerce predictions for 2022 – BetaNews
One of the side effects of the pandemic over the last two years has been a boom in online shopping. But is this something that's here to stay? And what are we likely to see happening in the eCommerce field in 2022?
Here is what some of the industrys experts think will be next year's trends.
"Chat Commerce is the third wave of digital commerce, following on from eCommerce and app commerce," says Pieter de Villiers, CEO and co-founder at Clickatell. "More than 7.7 billion people use some form of chat several times a day, making chat the largest digital engagement channel in the world. With COVID-19 accelerating digital commerce adoption and businesses fast tracking their digital transformation to meet consumers where they are, we can expect an increased demand for and deployment of Chat Commerce services and experiences."
Gabriel Straub, chief data scientist at Ocado Technology believes we'll see machine learning used to improve the customer experience.
Organizations can improve customers' experience by making the full interaction feel like a continuous conversation that flows naturally from end-to-end. For Ocado Technology, from a data point of view, this means thinking about the customer's journey through the shop from the selection of a delivery slot all the way to the checkout, rather than as a series of discrete interactions. Where in the customer flow would it make sense to add a bit of friction now, that will allow us to remove more friction later in the journey? Where can we explicitly ask a user about an assumption that we might have about them to ensure that we really understand their needs and preferences? And how do we make sure that the way we think about ML in a product covers the whole journey rather than just a single interaction or click?
Nowhere is this more important than in the world of grocery retail where an average basket size is significantly larger and shopping frequency much higher than in most other retail segments. Combining algorithms to create a more consistent user experience across the user journey will be a key focus.
Nathanael Coffing, CSO and co-founder of Cloudentity thinks privacy and consumer control over data will be a key factor. "Consumers today are calling for more control over their online data and how its being used by companies. While government regulators enforcing privacy laws such as GDPR, CCPA and CPRA are a step in the right direction, more needs to be done to protect consumers privacy and this needs to start at registration and continue through API-based data sharing. Every website or app should display an icon (similar to SSL) as soon as a user opens the page that rates the certifications the company is meeting to protect their customers' data. These must be written in a way that is easy for consumers to understand as well -- no hiding behind confusing legal jargon. Then, organizations will have no choice but to be transparent with how they are harvesting, using and sharing their users' data. The icon must provide consumers the ability to control their privacy settings on an attribute level, control their sharing of that attribute and delete their data after they are done with the website/app, so the user remains in control of their personal information at all times."
Stanley Huang, CTO and co-founder of Moxtra believes we'll see a blurring of boundaries between consumer-facing and backend systems. "Backend systems are designed to manage internal data, processes and operations in an organized fashion. They help determine which ROI measurements are about productivity, management efficiency and cost savings. On the other side, customer communication channels are designed as unstructured, data agnostic utilities that often focus on omni-channel and bots to understand customer intent. In 2022, businesses will need to blend the backend and customer communication channels together to ensure data and communication are deeply coupled to offer the best customer experience possible. When businesses think about the customer experience holistically with a customer-centric backend leading to a more analytical frontend channel, the customer experience will be improved across the entire lifecycle."
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Chat Commerce, machine learning and a stronger privacy focus eCommerce predictions for 2022 - BetaNews
Grants totaling $4.6 million support the use of machine learning to improve outcomes of people with HIV – Brown University
PROVIDENCE, R.I.[Brown University] Over the past four decades of treating HIV/AIDS, two important facts have been established: HIV-positive patients need to be put on treatment as soon as theyre diagnosed and then kept on an effective treatment plan. This response can help turn HIV into a chronic but manageable disease and can essentially help people live normal, healthy lives, said Joseph Hogan a professor of public health and of biostatistics at Brown University, who has been researching HIV/AIDS for 25 years.
Hogan is one of the primary investigators on two recently awarded grants from the National Institutes of Health, totaling nearly $4.6 million over five years, to support the creation and utilization of data-driven tools that will allow care programs in Kenya to meet these key treatment goals.
If the system works as designed, then we have confidence that well improve the health outcomes of people with HIV, Hogan said.
The first part of the project involves using data science to understand whats called the HIV care cascade, said Hogan, who is the co-director of the biostatistics program for Academic Model Providing Access to Healthcare (AMPATH), a consortium of 14 North American universities who collaborate with Moi University in Eldoret, Kenya, on HIV research, care and training.
Hogan will collaborate with longtime scientific partner Ann Mwangi, associate professor of biostatistics at Moi University, who received a Ph.D. in biostatistics from Brown in 2011. Using AMPATH-developed electronic health record database, a team co-led by Hogan and Mwangi will develop algorithm-based statistical machine learning tools to predict when and why patients might drop out of care and when their viral load levels indicate they are at risk of treatment failure.
These algorithms, Hogan said, will then be integrated into the electronic health record system to deliver the information at the point of care, through handheld tablets that the physicians can use when sitting in the exam room with the patient. In consultation with experts in user interface design, the team will assess and test the most effective ways to communicate the results of the algorithm to the care providers so that they can use them to make decisions about patient care, Hogan said.
The predictive modeling system the team is developing, Hogan said, will alert a physician to red flags in the patients treatment plan at the point of care. This way, interventions can be developed to help a patient get to their treatment appointments, for example, before the patient needs to miss or cancel them. Or if a patient is predicted to have high viral load, Hogan said, a clinician can refer them for additional monitoring to identify and treat the increase before it becomes a problem.
New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests – UC Davis Health
(SACRAMENTO)
UC Davis Health, in partnership with SpectraPass, is evaluating a new type of rapid COVID-19 test. The research will involve about 2,000 people in Sacramento and Las Vegas.
The idea behind the new platform is a scalable system that can quickly and accurately perform on-site tests for hundreds or potentially thousands of people.
Nam Tran is a professor of clinical pathology in the UC Davis School of Medicine and a co-developer of the novel testing platform with SpectraPass, a Las Vegas-based startup.
Tran explained that the system doesnt look for the SARS-CoV-2 virus like a PCR test does. Instead, it detects an infection by analyzing the bodys response to it. When ill, the body produces differing protein profiles in response to infection. These profiles may indicate different types of infection, which can be detected by machine learning.
The goal of this study is to have enough COVID-19 positive and negative individuals to train our machine learning algorithm to identify patients infected by SARS-CoV-2, said Tran.
A study published by Tran and his colleagues earlier this year in Nature Scientific Reports found the novel method to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests.
In addition to identifying positive cases of COVID-19, the platform also uses next-generation sequencing to confirm multiple respiratory pathogens like the flu and the common cold.
The sequencing panel at UC Davis Health can detect over 280 respiratory pathogens, including SARS-CoV-2 and related variants allowing the study to train the machine-learning algorithms to differentiate COVID-19 from other respiratory diseases.
So far, the study has not seen any participants with the new omicron variant.
Our team has tested the system with samples from patients infected with delta and other variants of the SARS-CoV-2 virus. We are fairly certain that omicron will be detected as well, but we wont know for sure until we encounter a study participant with the variant, Tran said.
The Emergency Department (ED) at the UC Davis Medical Center is conducting the testing in Sacramento. Collection for testing in Las Vegas is conducted at multiple businesses and locations.
The team expects the study will continue until the end of winter. The results from the new study will be used to seek emergency use authorization (EUA) from the Food and Drug Administration.
The novel testing system uses an analytical instrument known as a mass spectrometer. Its paired with machine learning algorithms produced by software called the Machine Intelligence Learning Optimizer or MILO. MILO was developed by Tran, Hooman Rashidi, a professor in the Department of Pathology and Laboratory Medicine, and Samer Albahra, assistant professor and medical director of pathology artificial intelligence in the Department of Pathology and Laboratory Medicine.
As with many other COVID-19 tests, a nasal swab is used to collect a sample. Proteins from the nasal sample are ionized with the mass spectrometers laser, then measured and analyzed by the MILO machine learning algorithms to generate a positive or negative result.
In addition to conducting the mass spectrometry testing, UC Davis serves as a reference site for the study, performing droplet digital PCR (ddPCR) tests, the gold standard for COVID-19 testing, to assess the accuracy of the mass spectrometry tests.
The project originated with Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company and founder of SpectraPass. Gallagher is also a UC Davis alumnus and a longtime supporter of innovation and entrepreneurship at UC Davis.
In 2020, when the COVID-19 pandemic brought the travel and hospitality industries almost to a standstill, Gallagher began conceptualizing approaches to allow people to gather again safely. He teamed with researchers at UC Davis Health to develop the new platform and launched SpectraPass.
In addition to the novel testing solution, SpectraPass is also developing digital systems to accompany the testing technology. Those include tools to authenticate and track verified test results from the system so an individual can access and use them. The goal is to facilitate accurate, large-scale rapid testing that will help keep businesses and the economy open through the current and any future pandemics.
The official start of our multi-center study across multiple locations marks an important milestone in our journey at SpectraPass. We are excited to test and generate data on a broader scale. Our goal is to move the platform from a promising new technology to a proven solution that can ultimately benefit the broader population, said Greg Ourednik, president of SpectraPass.
New rapid COVID-19 test the result of university-industry partnership
Meet MILO, a powerful machine learning AI tool from UC Davis Health
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New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests - UC Davis Health
Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% 2021: Global Industry Insights by Global Players, Regional Segmentation,…
Machine Learning as a Service (MLaaS) market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Machine Learning as a Service (MLaaS) market by region.
Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 2017-2023.
Machine Learning as a Service in Manufacturing Market Global Drivers Restraints Opportunities Trends and Forecasts up to 2023
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Market OverviewMachine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality ensure supply chain efficiency reduce time to market fulfil reliability standards and thus enhance their customer base through the application of machine learning.
Machine learning algorithms offer predictive insights at every stage of the production which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.
Market AnalysisAccording to Reportocean Research Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 2017-2023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing high volume of structured and unstructured data the integration of machine learning with big data and other technologies the rising importance of predictive and preventive maintenance and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges the dearth of skilled data scientists and data inaccessibility and security concerns to name a few.
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Segmentation by ComponentsThe market has been analyzed and segmented by the following components Software Tools Cloud and Web-based Application Programming Interface (APIs) and Others.
Segmentation by End-usersThe market has been analyzed and segmented by the following end-users namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.?
Segmentation by Deployment ModeThe market has been analyzed and segmented by the following deployment mode namely public and private.
Regional AnalysisThe market has been analyzed by the following regions as Americas Europe APAC and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China India Japan and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.
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Vendor AnalysisSome of the key players in the market are Microsoft Amazon Web Services Google Inc. and IBM Corporation. The report also includes watchlist companies such as BigML Inc. Sight Machine Eigen Innovations Inc. Seldon Technologies Ltd. and Citrine Informatics Inc.
BenefitsThe study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry the report aims to provide an opportunity for players to understand the latest trends current market scenario government initiatives and technologies related to the market. In addition it helps the venture capitalists in understanding the companies better and take informed decisions.> The report covers drivers restraints and opportunities (DRO) affecting the market growth during the forecast period (2017-2023).> It also contains an analysis of vendor profiles which include financial health business units key business priorities SWOT strategy and views.> The report covers competitive landscape which includes M&A joint ventures and collaborations and competitor comparison analysis.> In the vendor profile section for the companies that are privately held financial information and revenue of segments will be limited.
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Region/Country Cover in the Report
Regions -Americas Europe APAC and MEA
Key Players Covered in the Report
Microsoft Amazon Web Services Google Inc. and IBM Corporation
This report covers aspects of the regional analysis market.The report includes data about North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa.This report analyzes current and future market trends by region, providing information on product usage and consumption.Reports on the market include the growth rate of every region, based on their countries over the forecast period.
What factors are taken into consideration when assessing the key market players?
The report analyzes companies across the globe in detail.The report provides an overview of major vendors in the market, including key players.Reports include information about each manufacturer, such as profiles, revenue, product pricing, and other pertinent information about the manufactured products.This report includes a comparison of market competitors and a discussion of the standpoints of the major players.Market reports provide information regarding recent developments, mergers, and acquisitions involving key players.
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What are the key findings of the report?This report provides comprehensive information on factors expected to influence the market growth and market share in the future.The report offers the current state of the market and future prospects for various geographical regions.This report provides both qualitative and quantitative information about the competitive landscape of the market.Combined with Porters Five Forces analysis, it serves as SWOT analysis and competitive landscape analysis.It provides an in-depth analysis of the market, highlighting its growth rates and opportunities for growth.
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Top Computer Vision Jobs to Apply in December 2021 – Analytics Insight
You can apply for these computer vision jobs this December
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong in an image.
New Delhi
Endovision is a Hong Kong-based med-tech company, which is helping endoscopists to reduce cancer miss rates with the aid of real-time video analysis using AI. We have generated interest worldwide, and this is the hottest area of research in the field of endoscopy. Our partners are located in Hong Kong, Japan, and India, with the primary focus on Hong Kong at the moment. They are looking to hire a new Research Engineer in our team. Youd help create AI-first products implementing state-of-the-art research papers, contributing to the company IP, and technology stack deployed in Nvidia Jetson ecosystem. Youd work on cutting-edge deep learning, computer vision, and graphics problems with an emphasis on endoscopy, with an opportunity to collaborate with research scientists and engineers at the Endovision and its partnering institutions. Candidates should have: Experience (academic or industry) with computer vision and deep learning in at least two Neural networks CNNs, RNNs, autoencoders, etc., and transfer learning generative deep learning method, esp. for image generation from images and videos numerical optimization.
Apply here.
Gurugram, Haryana
Profile Description:
A passionate developer with a drive to work in a hot startup. You will be working in a team in the area of computer vision, machine learning, algorithm design, security, and hardware in a fast-paced and dynamic environment. You will get the opportunity to showcase your talents and capabilities in development, research, and technical operations. The ability to multi-task, solve challenging problems, learn new technology areas quickly, persistence, hard work, and humility are necessary prerequisites to fulfill your role.
Apply here.
Chennai, Tamil Nadu
The company is creating a home design solution (AI) A platform to explore new Architectural designs for consumers.
It is an upcoming organization in need of staff.
Apply here.
Bengaluru
Experience: 2-5 years;
Computer Vision and Image Processing: In this position, you will be involved in the given Roles and Responsibilities Roles and Responsibilities Working on computer vision/image processing applications like object classification, segmentation, etc. Working on deep learning algorithms for machine vision-based inspection use cases. Working on Machine Vision Cameras involving a variety of Lens, Filters, and other Optical Elements to collect data. Having full-stack development experience will be a plus. Educational Qualification Bachelors degree in marketing, business or related field. Skill(s) required OpenCV, Python, Linux and C programming, Python, PHP, Django, cloud, TensorFlow, machine vision camera experience will be a bonus.
Image processing, algorithms, Python, c programming, machine vision, Django, Linux, PHP, OpenCV
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Education Requirements:
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LiveFreely Announces Apple Watch Version of ‘BUDDY,’ the Predictive AI-Driven Digital Health Assistant for Seniors and Their Loved Ones – Yahoo…
Already compatible with Fitbit smartwatches, the personal health management and remote monitoring technology is now also available for Apple smartwatches
BUDDY, Your Personal Health Assistant
SAN JOSE, Calif., Dec. 22, 2021 (GLOBE NEWSWIRE) -- LiveFreely, Inc., a Silicon Valley digital health company that develops innovative technology to improve the health and well-being of seniors and their loved ones, today launched BUDDY for Apple Watches. The BUDDY app uses AI and machine learning to predict, prevent, and detect health challenges while providing support and data for seniors and their caregivers.
"BUDDY works with Fitbit, and we've been testing it on the Apple platform," says Dr. Arthur Jue, co-founder and CEO of LiveFreely. "We are pleased to now make BUDDY commercially available on both platforms a milestone in our efforts to help empower seniors to age more proactively."
BUDDY bundles a full spectrum of functions that assist users with health issues, from predicting falls to monitoring irregular health patterns to detecting wandering. Its suite of solutions addresses critical health issues faced by seniors and caregivers, including:
Predicting and preventing falls, the leading cause of death among seniors, through AI and machine learning that triggers alerts when changes in gait are detected
Automatic fall detection and alerts
Irregular health pattern detection and alerts
Location alerts that help dementia and Alzheimer's wanderers
"Code blue" alerts for cardiac events
Medication adherence and schedule reminders
User-friendly and easy to set up, the app was conceived by Jue and his brother Daniel after caring for their aging father. "While our team has worked tirelessly on BUDDY for Apple Watch, we've kept foremost in mind the many mothers, fathers, grandmothers, grandfathers, aunts, uncles, and loved ones who urgently need this technology," says Daniel Jue, LiveFreely co-founder and CTO.
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As caregiving becomes increasingly complex and expensive, the LiveFreely team designed BUDDY to be an affordable, effective way to enhance independence while empowering caregivers to respond quickly when seconds count. BUDDY is the first app to send real-time data to emergency services personnel en route in an ambulance.
LiveFreely is offering a special limited-time promotion of only $4.99 per month subscription for BUDDY. Go to http://www.buddylife.com/launch to get the special promo code. LiveFreely will also donate a portion of the proceeds to Project WeHOPE initiatives for the unhoused this holiday season.
Kerri Kasem, radio host and founder of Kasem Cares, says, "I'm thrilled that BUDDY is now available for the Apple Watch. I'm a huge fan. Had BUDDY been available when my dad Casey Kasem (American Top 40) developed dementia, things would have turned out a lot differently. I believe BUDDY is a lifesaver."
Ninety-two-year-old BUDDY user Calvin Wong agrees. "BUDDY has been a great help to my family and me because they know I'm safe. I'm at an age where I have a fear of falling. I've fallen a few times already because I only have one eye, mono-vision, and can't tell distance. I might be stepping off a curb and not know it. So, BUDDY helps a lot."
About BUDDY by LiveFreely
Through machine learning and artificial intelligence, BUDDY monitors and manages factors such as fall prediction, prevention, and detection, medication schedules and reminders, GPS location, and emergency notifications. The platform alerts smartwatch wearers, family members, caregivers, first responders, and emergency services providers of irregularities, enhancing the security, connectedness, and independence of loved ones. To learn more about BUDDY by LiveFreely, visit http://www.buddylife.com.
Press Inquiries:Janet ChongInfo@livefreely.today
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These are the top priorities for tech executives in 2022, survey reveals – CNBC
Big software IPOs, cyberattacks and the push into the metaverse were just some of the themes coming out of the technology sector in 2021.
As technology executives look towards the year ahead, they say things like artificial intelligence, cloud computing and machine learning will be critically important to their companies in 2022, according to a recent CNBCTechnology Executive Council survey of 44 executives.
Here's a breakdown from the CNBC TEC survey of the technologies expected to receive the most time and money.
A vast majority (81%) of executives said that artificial intelligence would either be critically important or very important to their companies in 2022.
Twenty percent of respondents also said that AI is the technology that they expect to invest the most resources in over the next 12 months.
The emphasis on cloud computing shows no signs of lessening in the year ahead, as 82% of respondents said that the technology would be critically important to their company in 2022. It is also the technology where the most executives (34%) said their companies would be investing the most money.
Ninety-one percent of executives said that machine learning would be critically or very important to their companies in 2022, while 20% said this would be the area they will invest the most money in.
It is also the technology that the most executives (18%) said they would be the most excited to see grow and develop in the year ahead.
No code and low code software was the technology that saw the second-highest amount of executives (11%) say they were most excited to see it grow and develop in 2022.
Other technologies that were highlighted by multiple executives include explainable AI, robotics and software-defined security.
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These are the top priorities for tech executives in 2022, survey reveals - CNBC
Machine Learning as a Service (MLaaS) Market 2021: Big Things are Happening in Development and Future Assessment by 2031 – Digital Journal
Pune, Maharashtra, India, December 17 2021 (Wiredrelease) Prudour Pvt. Ltd :High Use Of Machine Learning as a Service (MLaaS) Market|Better Business Growth, A One-Stop Guide For Growing Business In 2021
The Machine Learning as a Service (MLaaS) Market economy has improved over the last few years. There have been more entrants and technological advancement, as well as a growing rate of expansion due to the measures taken against short-term economic downturns. This report has been based on a few different types of research. The findings have been obtained from both primary and secondary tools for gathering data. The study is a perfect blend of qualitative and quantifiable information, highlighting key market developments as well industry challenges in gap analysis with new opportunities that could be trending. A variety of graphical presentation techniques are used to demonstrate the facts.
The report provides a comprehensive description of Machine Learning as a Service (MLaaS) market that presents an overview of the global market. The information in this document includes a forecast (2021-2031), trends drivers both current and future as good opinions from industry professionals on these topics with technological advancements and new entry explorations, many people are looking for economic countermeasures to increase their growth rates. The competitive nature of the industry is forcing key players to focus on new merger and acquisition methods in order to maintain their power over market share.
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The influential players covered in this report are:
GoogleIBM CorporationMicrosoft CorporationAmazon Web ServicesBigMLFICOYottamine AnalyticsErsatz LabsPredictron LabsH2O.aiAT and TSift Science
Figure:
Topographical segmentation of Machine Learning as a Service (MLaaS) market by top product type, best application, and key region:
Segmentation by Type:
Software ToolsCloud and Web-based Application Programming Interface (APIs)Other
Segmentation by Application:
ManufacturingRetailHealthcare and Life SciencesTelecomBFSIOther (Energy and Utilities, Education, Government)
Machine Learning as a Service (MLaaS) Market: Regional Segment Analysis
North America (USA, Canada, and Mexico)
Europe (Russia, France, Germany, UK, and Italy)
Asia-Pacific (China Korea, India, Japan, and Southeast Asia)
South America (Brazil, Columbia, Argentina, etc)
The Middle East and Africa (Nigeria, UAE, Saudi Arabia, Egypt, and South Africa)
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The latest mechanical enhancements and Machine Learning as a Service (MLaaS) new releases to engage our consumers to produce, settle on instructed business decisions, and build their future expected achievements.
Machine Learning as a Service (MLaaS) market focuses more on future methodology changes, current business and progressions and open entryways for the global market.
The investment return analysis, SWOT analysis, and feasibility study are also used for Machine Learning as a Service (MLaaS) market data analysis.
Key Highlights of the Machine Learning as a Service (MLaaS) Market Research Report:
1. The report summarizes the machine learning as a service (mlaas) Market by stating the basic product definition, the number of product applications, product scope, product cost and price, supply and demand ratio, market overview.
2. Competitive landscape of all leading key players along with their business strategies, approaches, and latest machine learning as a service (mlaas) market movements.
3. It elements market feasibility investment, opportunities, the growth factors, restraints, market risks, and machine learning as a service (mlaas) business driving forces.
4. It performs a comprehensive study of emerging players of machine learning as a service (mlaas) business along with the existing ones.
5. It accomplishes primary and secondary research and resources to estimate top products, market size, and industrial partnerships of machine learning as a service (mlaas) business.
6. Global Machine Learning as a Service (MLaaS) market report ends by articulating research findings, data sources, results, list of dealers, sales channels, businesses and distributors along with an appendix.
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Key questions include:
1. What can we estimate about the anticipated growth rates and also the global machine learning as a service (mlaas) industry size by 2031?
2. Who investors will use the specifics of our research, as well as some key parameters and forecast periods to guide their investment decisions?
3. What will happen in the coming existing and emerging markets?
4. All those vendors who make a profit; some do not.
5. What would be the upcoming machine learning as a service (mlaas) market behavior forecast with trends, challenges, and drivers challenges for development?
6. What industry opportunities and dangers are faced by vendors in the market?
7. Which would be machine learning as a service (mlaas) industry opportunities and challenges faced with most vendors in the market?
8. What are the variables affecting the machine learning as a service (mlaas) market share?
9. What will be the outcomes of this market SWOT five forces analysis?
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