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Database Administrator Courses

Database professionals are in high demand. If you already work as one, you probably know this. And if you are looking to become a database administrator, that high demand and the commensurate salary may be what is motivating you to make this career move. 

How can you advance your career as a database administrator? By taking the courses on this list.

If you want to learn more about database administration to expand your knowledge and move up the ladder in this field, these courses can help you achieve that goal.

Oracle DBA 11g/12c – Database Administration for Junior DBA from Udemy

Udemy’s Oracle DBA 11g/12c – Database Administration for Junior DBA course can help you get a high-paying position as an Oracle Database Administrator. 

Best of all, it can do it in just six weeks.

This database administrator course is a Udemy bestseller that is offered in eight languages. Over 29,000 students have taken it, giving it a 4.3-star rating. Once you complete it and become an Oracle DBA, you will be able to:

  • Install the Oracle database.
  • Manage Tablespace.
  • Understand database architecture.
  • Administer user accounts.
  • Perform backup and recovery.
  • Diagnose problems.

To take the intermediate-level course that includes 11 hours of on-demand video spanning 129 lectures, you should have basic knowledge of UNIX/LINUX commands and SQL.

70-462: SQL Server Database Administration (DBA)

The 70-462: SQL Server Database Administration (DBA) course from Udemy was initially designed to help beginner students ace the Microsoft 70-462 exam. Although that test has been officially withdrawn, you can still use this course to gain some practical experience with database administration in SQL Server.

Many employers seek SQL Server experience since it is one of the top database tools. Take the 70-462: SQL Server Database Administration (DBA) course, and you can gain valuable knowledge on the course and supply your resume a nice boost.

Some of the skills you will learn in the 70-462 course include:

  • Managing login and server roles.
  • Managing and configuring databases.
  • Importing and exporting data.
  • Planning and installing SQL Server and related services.
  • Implementing migration strategies.
  • Managing SQL Server Agent.
  • Collecting and analyzing troubleshooting data.
  • Implementing and maintaining indexes.
  • Creating backups.
  • Restoring databases.

DBA knowledge is not needed to take the 10-hour course that spans 100 lectures, and you will not need to have SQL Server already installed on your computer. In terms of popularity, this is a Udemy bestseller with a 4.6-star rating and over 20,000 students.

MySQL Database Administration: Beginner SQL Database Design from Udemy

Nearly 10,000 students have taken the MySQL Database Administration: Beginner SQL Database Design course on Udemy, making it a bestseller on the platform with a 4.6-star rating.

The course features 71 lectures that total seven hours in length and was created for those looking to gain practical, real-world business intelligence and analytics skills to eventually create and maintain databases.

What can you learn from taking the Beginner SQL Database Design course? Skills such as:

  • Connecting data between tables.
  • Assigning user roles and permissions.
  • Altering tables by removing and adding columns.
  • Writing SQL queries.
  • Creating databases and tables with the MySQL Workbench UI.
  • Understanding common Relational Database Management Systems.

The requirements for taking this course are minimal. It can help to have a basic understanding of database fundamentals, and you will need to install MySQL Workbench and Community Server on your Mac or PC.

Database Administration Super Bundle from TechRepublic Academy

If you want to immerse yourself into the world of database administration and get a ton of bang for your buck, TechRepublic Academy’s Database Administration Super Bundle may be right up your alley.

It gives you nine courses and over 400 lessons equaling over 86 hours that can put you on the fast track to building databases and analyzing data like a pro. A sampling of the courses offered in this bundle include:

  • NoSQL MongoDB Developer
  • Introduction to MySQL
  • Visual Analytics Using Tableau
  • SSIS SQL Server Integration Services
  • Microsoft SQL Novice To Ninja
  • Regression Modeling With Minitab

Ultimate SQL Bootcamp from TechRepublic Academy

Here is another bundle for database administrators from TechRepublic Academy. With the Ultimate SQL Bootcamp, you get nine courses and 548 lessons to help you learn how to:

  • Write SQL queries.
  • Conduct data analysis.
  • Master SQL database creation.
  • Use MySQL and SQLite
  • Install WAMP and MySQL and use both tools to create a database.

Complete Oracle Master Class Bundle from TechRepublic Academy

The Complete Oracle Master Class Bundle from TechRepublic Academy features 181 hours of content and 17 courses to help you build a six-figure career. This intermediate course includes certification and will supply you hands-on and practical training with Oracle database systems.

Some of the skills you will learn include:

  • Understanding common technologies like the Oracle database, software testing, and Java.
  • DS and algorithms.
  • RDBMS concepts.
  • Troubleshooting.
  • Performance optimization.

Learn SQL Basics for Data Science Specialization from Coursera

Coursera’s Learn SQL Basics for Data Science Specialization course has nearly 7,000 reviews, giving it a 4.5-star rating. Offered by UC Davis, this specialization is geared towards beginners who lack coding experience that want to become fluent in SQL queries.

The specialization takes four months to complete at a five-hour weekly pace, and it is broken down into four courses:

  1. SQL for Data Science
  2. Data Wrangling, Analysis, and AB Testing with SQL
  3. Distributed Computing with Spark SQL
  4. SQL for Data Science Capstone Project

Skills you can gain include:

  • Data analysis
  • Distributed computing using Apache Spark
  • Delta Lake
  • SQL
  • Data science
  • SQLite
  • A/B testing
  • Query string
  • Predictive analytics
  • Presentation skills
  • Creating metrics
  • Exploratory data analysis

Once finished, you will be able to analyze and explore data with SQL, write queries, conduct feature engineering, use SQL with unstructured data sets, and more.

Relational Database Administration (DBA) from Coursera

IBM offers the Relational Database Administration (DBA) course on Coursera with a 4.5-star rating. Complete the beginner course that takes approximately 19 hours to finish, and it can count towards your learning in the IBM Data Warehouse Engineer Professional Certificate and IBM Data Engineering Professional Certificate programs.

Some of the skills you will learn in this DBA course include:

  • Troubleshooting database login, configuration, and connectivity issues.
  • Configuring databases.
  • Building system objects like tables.
  • Basic database management.
  • Managing user roles and permissions.
  • Optimizing database performance.

Oracle Autonomous Database Administration from Coursera

Offered by Oracle, the Autonomous Database Administration course from Coursera has a 4.5-star rating and takes 13 hours to complete. It is meant to help DBAs deploy and administer Autonomous databases. Finish it, and you will prepare yourself for the Oracle Autonomous Database Cloud Certification.

Some of the skills and knowledge you can learn from this course include:

  • Oracle Autonomous Database architecture.
  • Oracle Machine Learning.
  • SQL Developer Web.
  • APEX.
  • Oracle Text
  • Autonomous JSON.
  • Creating, deploying, planning, maintaining, monitoring, and implementing an Autonomous database.
  • Migration options and considerations.

Looking for more database administration and database programming courses? Check out our tutorial: Best Online Courses to Learn MySQL.

Disclaimer: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit our Terms of Use page.

Thu, 21 Jul 2022 16:35:00 -0500 en-US text/html https://www.databasejournal.com/ms-sql/database-administrator-courses/
Killexams : Apple and IBM Unveil First Part of Relationship -- What It Looks Like No result found, try new keyword!Apple and IBM unveil the first benefits of their working ... "The retention app for insurance might be used for learning what your best sellers do," she said. "The customization of these apps ... Thu, 04 Aug 2022 12:00:00 -0500 en-us text/html https://www.thestreet.com/technology/apple-and-ibm-unveil-first-part-of-relationship-what-it-looks-like-12980797 Killexams : Quantum Computer Systems II No result found, try new keyword!In this course, students will learn to work with the IBM Qiskit software tools to write ... compiler, circuit optimization, python, qiskit, quantum algorithms, quantum technology, superposition ... Wed, 15 Dec 2021 00:48:00 -0600 text/html https://www.usnews.com/education/skillbuilder/quantum-computer-systems-ii-1_course_v1:UChicagoX+QCS12000+1T2022_verified Killexams : Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026
Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

“IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK).”

Quantum Computing Software Market by Component (Software, Services), Deployment Mode (Cloud, On-Premises), Organization Size, Technology, Application (Optimization, Simulation), Vertical (BFSI, Government), and Region – Global Forecast to 2026

The Quantum Computing Software Market size is projected to grow from USD 0.11 billion in 2021 to 0.43 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period. The major factors driving the growth of the Quantum Computing Software market include the growing adoption of quantum computing software in the BFSI vertical, government support for the development and deployment of the technology, and the increasing number of strategic alliances for research and development.

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Based on Component, the service segment to grow at a higher CAGR during the forecast period

Among the component segment, the services segment is leading the quantum computing software market in 2021. The growth of the services segment can be attributed to the increasing investments by start-ups in research and development related to quantum computing technology. Quantum computing software and services are used in optimization, simulation, and machine learning applications, thereby leading to optimum utilization costs and highly efficient operations in various industries.

Based on application, the optimization segment is expected to hold the highest market size during the forecast period

The optimization segment is expected to lead the global quantum computing software market in terms of market share. Optimization problems exist across all industries and business functions. Some of these problems take too long to be solved optimally with traditional computers, where the usage of quantum computing technology is expected to be an optimum solution. Several optimization problems require a global minimal point solution. By using quantum annealing, the optimization problems can be solved earlier as compared to supercomputers.

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Major Quantum Computing Software vendors include IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK), Zapata Computing (US), Quantum Circuits, Inc. (US), Quantica Computacao (India), XANADU Quantum Technologies (Canada), VeriQloud (France), Quantastica (Finland), AVANETIX (Germany), Kuano (England), Rahko (UK), Ketita Labs (Estonia), and Aliro Quantum (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

IBM was founded in 1911 and is headquartered in New York, US. It is a multinational technology and consulting corporation that offers infrastructure, hosting, and consulting services. The company operates through five major business segments: Cloud and Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. IBM Cloud has emerged as a platform of choice for all business applications, as it is AI compatible. It is a unifying platform that integrates IBM’s capabilities with a single architecture and spans over public and private cloud platforms. With this powerful cloud platform, the company can cater to the requirements of different businesses across the globe. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining. The company has a strong presence in the Americas, Europe, MEA, and APAC and clients in more than 175 countries. IBM is one of the major players in the quantum computing ecosystem. The company in 2016 made a quantum computer available to the public by connecting it to the cloud. In September 2019, it opened a Quantum Computation Center. The Quantum Computation Center offers about 100 IBM clients, academic institutions, and more than 200,000 registered users access to this cutting-edge technology through a collaborative effort called the IBM Q Network and Qiskit, IBM’s open-source development platform for quantum computing. Through these efforts, IBM is exploring the ways quantum computing can address the most complicated problems faced while training the workforce to use this technology.

Rigetti Computing was founded in 2013 and is headquartered in California, US. Rigetti Computing designs and manufactures superconducting quantum-integrated circuits. It develops quantum computers, as well as superconducting quantum processors that power them. The machines of the company can be integrated with any public, private, or hybrid cloud through the quantum cloud services (QCS) platform. It is a full-stack quantum computing company that provides an integrated computing environment. Rigetti Computing develops algorithms for quantum computing that focus on application areas such as machine learning, logistics, healthcare and pharmaceuticals, and chemicals. The company also delivers a set of tools, such as Quil, pyQuil, and Quilc, which help solve optimization problems.

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Killexams : IBM Research uses advanced computing to accelerate therapeutic and biomarker discovery

Over the past decade, artificial intelligence (AI) has emerged as an engine of discovery by helping to unlock information from large repositories of previously inaccessible data. The cloud has expanded computer capacity exponentially by creating a global network of remote and distributed computing resources. And quantum computing has arrived on the scene as a game changer in processing power by harnessing quantum simulation to overcome the scaling and complexity limits of classical computing.

In parallel to these advances in computing, in which IBM is a world leader, the healthcare and life sciences have undergone their own information revolution. There has been an explosion in genomic, proteomic, metabolomic and a plethora of other foundational scientific data, as well as in diagnostic, treatment, outcome and other related clinical data. Paradoxically, however, this unprecedented increase in information volume has resulted in reduced accessibility and a diminished ability to use the knowledge embedded in that information. This reduction is caused by siloing of the data, limitations in existing computing capacity, and processing challenges associated with trying to model the inherent complexity of living systems.

IBM Research is now working on designing and implementing computational architectures that can convert the ever-increasing volume of healthcare and life-sciences data into information that can be used by scientists and industry experts the world over. Through an AI approach powered by high-performance computing (HPC)—a synergy of quantum and classical computing—and implemented in a hybrid cloud that takes advantage of both private and public environments, IBM is poised to lead the way in knowledge integration, AI-enriched simulation, and generative modeling in the healthcare and life sciences. Quantum computing, a rapidly developing technology, offers opportunities to explore and potentially address life-science challenges in entirely new ways.

“The convergence of advances in computation taking place to meet the growing challenges of an ever-shifting world can also be harnessed to help accelerate the rate of discovery in the healthcare and life sciences in unprecedented ways,” said Ajay Royyuru, IBM fellow and CSO for healthcare and life sciences at IBM Research. “At IBM, we are at the forefront of applying these new capabilities for advancing knowledge and solving complex problems to address the most pressing global health challenges.”

Improving the drug discovery value chain

Innovation in the healthcare and life sciences, while overall a linear process leading from identifying drug targets to therapies and outcomes, relies on a complex network of parallel layers of information and feedback loops, each bringing its own challenges (Fig. 1). Success with target identification and validation is highly dependent on factors such as optimized genotype–phenotype linking to enhance target identification, improved predictions of protein structure and function to sharpen target characterization, and refined drug design algorithms for identifying new molecular entities (NMEs). New insights into the nature of disease are further recalibrating the notions of disease staging and of therapeutic endpoints, and this creates new opportunities for improved clinical-trial design, patient selection and monitoring of disease progress that will result in more targeted and effective therapies.

Accelerated discovery at a glance

Fig. 1 | Accelerated discovery at a glance. IBM is developing a computing environment for the healthcare and life sciences that integrates the possibilities of next-generation technologies—artificial intelligence, the hybrid cloud, and quantum computing—to accelerate the rate of discovery along the drug discovery and development pipeline.

Powering these advances are several core computing technologies that include AI, quantum computing, classical computing, HPC, and the hybrid cloud. Different combinations of these core technologies provide the foundation for deep knowledge integration, multimodal data fusion, AI-enriched simulations and generative modeling. These efforts are already resulting in rapid advances in the understanding of disease that are beginning to translate into the development of better biomarkers and new therapeutics (Fig. 2).

“Our goal is to maximize what can be achieved with advanced AI, simulation and modeling, powered by a combination of classical and quantum computing on the hybrid cloud,” said Royyuru. “We anticipate that by combining these technologies we will be able to accelerate the pace of discovery in the healthcare and life sciences by up to ten times and yield more successful therapeutics and biomarkers.”

Optimized modeling of NMEs

Developing new drugs hinges on both the identification of new disease targets and the development of NMEs to modulate those targets. Developing NMEs has typically been a one-sided process in which the in silico or in vitro activities of large arrays of ligands would be tested against one target at a time, limiting the number of novel targets explored and resulting in ‘crowding’ of clinical programs around a fraction of validated targets. exact developments in proteochemometric modeling—machine learning-driven methods to evaluate de novo protein interactions in silico—promise to turn the tide by enabling the simultaneous evaluation of arrays of both ligands and targets, and exponentially reducing the time required to identify potential NMEs.

Proteochemometric modeling relies on the application of deep machine learning tools to determine the combined effect of target and ligand parameter changes on the target–ligand interaction. This bimodal approach is especially powerful for large classes of targets in which active-site similarities and lack of activity data for some of the proteins make the conventional discovery process extremely challenging.

Protein kinases are ubiquitous components of many cellular processes, and their modulation using inhibitors has greatly expanded the toolbox of treatment options for cancer, as well as neurodegenerative and viral diseases. Historically, however, only a small fraction of the kinome has been investigated for its therapeutic potential owing to biological and structural challenges.

Using deep machine learning algorithms, IBM researchers have developed a generative modeling approach to access large target–ligand interaction datasets and leverage the information to simultaneously predict activities for novel kinase–ligand combinations1. Importantly, their approach allowed the researchers to determine that reducing the kinase representation from the full protein sequence to just the active-site residues was sufficient to reliably drive their algorithm, introducing an additional time-saving, data-use optimization step.

Machine learning methods capable of handling multimodal datasets and of optimizing information use provide the tools for substantially accelerating NME discovery and harnessing the therapeutic potential of large and sometimes only minimally explored molecular target spaces.

Focusing on therapeutics and biomarkers

Fig. 2 | Focusing on therapeutics and biomarkers. The identification of new molecular entities or the repurposing potential of existing drugs2, together with improved clinical and digital biomarker discovery, as well as disease staging approaches3, will substantially accelerate the pace of drug discovery over the next decade. AI, artificial intelligence.

Drug repurposing from real-world data

Electronic health records (EHRs) and insurance claims contain a treasure trove of real-world data about the healthcare history, including medications, of millions of individuals. Such longitudinal datasets hold potential for identifying drugs that could be safely repurposed to treat certain progressive diseases not easily explored with conventional clinical-trial designs because of their long time horizons.

Turning observational medical databases into drug-repurposing engines requires the use of several enabling technologies, including machine learning-driven data extraction from unstructured sources and sophisticated causal inference modeling frameworks.

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders in the world, affecting 1% of the population above 60 years of age. Within ten years of disease onset, an estimated 30–80% of PD patients develop dementia, a debilitating comorbidity that has made developing disease-modifying treatments to slow or stop its progression a high priority.

IBM researchers have now developed an AI-driven, causal inference framework designed to emulate phase 2 clinical trials to identify candidate drugs for repurposing, using real-world data from two PD patient cohorts totaling more than 195,000 individuals2. Extracting relevant data from EHRs and claims data, and using dementia onset as a proxy for evaluating PD progression, the team identified two drugs that significantly delayed progression: rasagiline, a drug already in use to treat motor symptoms in PD, and zolpidem, a known psycholeptic used to treat insomnia. Applying advanced causal inference algorithms, the IBM team was able to show that the drugs exert their effects through distinct mechanisms.

Using observational healthcare data to emulate otherwise costly, large and lengthy clinical trials to identify repurposing candidates highlights the potential for applying AI-based approaches to accelerate potential drug leads into prospective registration trials, especially in the context of late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Enhanced clinical-trial design

One of the main bottlenecks in drug discovery is the high failure rate of clinical trials. Among the leading causes for this are shortcomings in identifying relevant patient populations and therapeutic endpoints owing to a fragmented understanding of disease progression.

Using unbiased machine-learning approaches to model large clinical datasets can advance the understanding of disease onset and progression, and help identify biomarkers for enhanced disease monitoring, prognosis, and trial enrichment that could lead to higher rates of trial success.

Huntington’s disease (HD) is an inherited neurodegenerative disease that results in severe motor, cognitive and psychiatric disorders and occurs in about 3 per 100,000 inhabitants worldwide. HD is a fatal condition, and no disease-modifying treatments have been developed to date.

An IBM team has now used a machine-learning approach to build a continuous dynamic probabilistic disease-progression model of HD from data aggregated from multiple disease registries3. Based on longitudinal motor, cognitive and functional measures, the researchers were able to identify nine disease states of clinical relevance, including some in the early stages of HD. Retrospective validation of the results with data from past and ongoing clinical studies showed the ability of the new disease-progression model of HD to provide clinically meaningful insights that are likely to markedly Improve patient stratification and endpoint definition.

Model-based determination of disease stages and relevant clinical and digital biomarkers that lead to better monitoring of disease progression in individual participants is key to optimizing trial design and boosting trial efficiency and success rates.

A collaborative effort

IBM has established its mission to advance the pace of discovery in healthcare and life sciences through the application of a versatile and configurable collection of accelerator and foundation technologies supported by a backbone of core technologies (Fig. 1). It recognizes that a successful campaign to accelerate discovery for therapeutics and biomarkers to address well-known pain points in the development pipeline requires external, domain-specific partners to co-develop, practice, and scale the concept of technology-based acceleration. The company has already established long-term commitments with strategic collaborators worldwide, including the recently launched joint Cleveland Clinic–IBM Discovery Accelerator, which will house the first private-sector, on-premises IBM Quantum System One in the United States. The program is designed to actively engage with universities, government, industry, startups and other relevant organizations, cultivating, supporting and empowering this community with open-source tools, datasets, technologies and educational resources to help break through long-standing bottlenecks in scientific discovery. IBM is engaging with biopharmaceutical enterprises that share this vision of accelerated discovery.

“Through partnerships with leaders in healthcare and life sciences worldwide, IBM intends to boost the potential of its next-generation technologies to make scientific discovery faster, and the scope of the discoveries larger than ever,” said Royyuru. “We ultimately see accelerated discovery as the core of our contribution to supercharging the scientific method.”

Mon, 11 Apr 2022 04:28:00 -0500 en text/html https://www.nature.com/articles/d43747-022-00128-z
Killexams : Machine Learning as a Service Market Share, Industry Size, Growth, Sales, Opportunities, Analysis and Forecast To 2030

The MarketWatch News Department was not involved in the creation of this content.

Jul 18, 2022 (Alliance News via COMTEX) -- Key Companies Covered in the Machine Learning as a Service Market Research are Google Inc., SAS Institute Inc., FICO, Hewlett Packard Enterprise, Yottamine Analytics, Amazon Web Services, BigML, Inc., Microsoft Corporation, Predictron Labs Ltd., and IBM Corporation.and other key market players.

The global machine learning as a service market was valued at $571 million in 2016, and is projected to reach $5,537 million by 2023, growing at a CAGR of 39.0% from 2017 to 2023. Machine learning is a process of data analysis that comprises of statistical data analysis performed to derive desired predictive output without the implementation of explicit programming. It is designed to incorporate the functionalities of artificial intelligence (AI) and cognitive computing involving a series of algorithms and is used to understand the relationship between datasets to obtain a desired output. Machine learning as a service (MLaaS) incorporates range of services that offer machine learning tools through cloud computing services.

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Increased penetration of cloud-based solutions, growth associated with artificial intelligence and cognitive computing market, and increase in market for prediction solutions drive the market growth. In addition, growth in IT expenditure in emerging nations and technological advancements for workflow optimization fuel the demand for advanced analytical systems driving the machine learning as a service market growth. However, dearth of trained professionals is expected to impede the machine learning as a service market share. Furthermore, increased application areas and growth of IoT is expected to create lucrative opportunities for machine learning as a service market growth.

The global machine learning as a service market is segmented based on component, organization size, end-use industry, application, and geography. The component segment is bifurcated into software and services. Based on organization size, it is divided into large enterprises and small & medium enterprises. The application segment is categorized into marketing & advertising, fraud detection & risk management, predictive analytics, augmented & virtual reality, natural language processing, computer vision, security & surveillance, and others. On the basis of end-use industry, it is classified into aerospace & defense, IT & telecom, energy & utilities, public sector, manufacturing, BFSI, healthcare, retail, and others. By geography, the machine learning as a service market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

KEY BENEFITS FOR STAKEHOLDERS

This report provides an overview of the trends, structure, drivers, challenges, and opportunities in the global machine learning as a service market.
Porter’s Five Forces analysis highlights the potential of buyers & suppliers, and provides insights on the competitive structure of the market to determine the investment pockets.
Current and future trends adopted by the key market players are highlighted to determine overall competitiveness.
The quantitative analysis of the machine learning as a service market growth from 2017 to 2023 is provided to elaborate the market potential.

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Key Market Segments

By Component

Software
Services

By Organization Size

Large Enterprises
Small & Medium Enterprises

By End-Use Industry

Aerospace & Defence
IT & Telecom
Energy & Utilities
Public sector
Manufacturing
BFSI
Healthcare
Retail
Others

By Application

Marketing & Advertising
Fraud Detection & Risk Management
Predictive analytics
Augmented & Virtual reality
Natural Language processing
Computer vision
Security & surveillance
Others

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By Geography

North America
U.S.
Canada
Mexico
Europe
UK
France
Germany
Rest of Europe
Asia-Pacific
China
Japan
India
Rest of Asia-Pacific
LAMEA
Latin America
Middle East
Arica

Table of Content:

  • Market Definition and Overview
  • Research Method and Logic
  • Market Competition Analysis
  • Product and Service Analysis
  • Strategies for Company to Deal with the Impact of COVID-19
  • Market Segment by Type, Historical Data and Market Forecasts
  • Market Segment by Application, Historical Data and Market Forecasts
  • Market by by Region, Historical Data and Market Forecasts
  • Market Dynamic Analysis and Development Suggestions

Key Questions Answered in the Market Report

  • Which Manufacturing Technology is used for Market? What Developments Are Going on in That Technology?
  • Which Trends Are Causing These Developments? Who Are the Global Key Players in This Market?
  • What are Their Company Profile, Their Product Information, and Contact Information?
  • What Was Global Status of Market? What Was Capacity, Production Value, Cost and PROFIT of Market?
  • What Is Current Market Status of market Industry? What's Market Competition in This Industry, Both Company, and Country Wise?
  • What's Market Analysis of Market by Taking Applications and Types in Consideration?
  • What Are Projections of Global Market Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit?
  • What Will Be Market Share Report, Supply and Consumption? What about Import and Export?
  • What Is Market Chain Analysis by Upstream Raw Materials and Downstream Industry?

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COMTEX_410457729/2796/2022-07-18T06:21:38

The MarketWatch News Department was not involved in the creation of this content.

Mon, 18 Jul 2022 16:27:00 -0500 en-US text/html https://www.marketwatch.com/press-release/machine-learning-as-a-service-market-share-industry-size-growth-sales-opportunities-analysis-and-forecast-to-2030-2022-07-18
Killexams : CIOReview Names Cobalt Iron Among 10 Most Promising IBM Solution Providers 2022

LAWRENCE, Kan.--(BUSINESS WIRE)--Jul 28, 2022--

Cobalt Iron Inc., a leading provider of SaaS-based enterprise data protection, today announced that the company has been deemed one of the 10 Most Promising IBM Solution Providers 2022 by CIOReview Magazine. The annual list of companies is selected by a panel of experts and members of CIOReview Magazine’s editorial board to recognize and promote innovation and entrepreneurship. A technology partner for IBM, Cobalt Iron earned the distinction based on its Compass ® enterprise SaaS backup platform for monitoring, managing, provisioning, and securing the entire enterprise backup landscape.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220728005043/en/

Cobalt Iron Compass® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection. (Graphic: Business Wire)

According to CIOReview, “Cobalt Iron has built a patented cyber-resilience technology in a SaaS model to alleviate the complexities of managing large, multivendor setups, providing an effectual humanless backup experience. This SaaS-based data protection platform, called Compass, leverages strong IBM technologies. For example, IBM Spectrum Protect is embedded into the platform from a data backup and recovery perspective. ... By combining IBM’s technologies and the intellectual property built by Cobalt Iron, the company delivers a secure, modernized approach to data protection, providing a ‘true’ software as a service.”

Through proprietary technology, the Compass data protection platform integrates with, automates, and optimizes best-of-breed technologies, including IBM Spectrum Protect, IBM FlashSystem, IBM Red Hat Linux, IBM Cloud, and IBM Cloud Object Storage. Compass enhances and extends IBM technologies by automating more than 80% of backup infrastructure operations, optimizing the backup landscape through analytics, and securing backup data, making it a valuable addition to IBM’s data protection offerings.

CIOReview also praised Compass for its simple and intuitive interface to display a consolidated view of data backups across an entire organization without logging in to every backup product instance to extract data. The machine learning-enabled platform also automates backup processes and infrastructure, and it uses open APIs to connect with ticket management systems to generate tickets automatically about any backups that need immediate attention.

To ensure the security of data backups, Cobalt Iron has developed an architecture and security feature set called Cyber Shield for 24/7 threat protection, detection, and analysis that improves ransomware responsiveness. Compass is also being enhanced to use several patented techniques that are specific to analytics and ransomware. For example, analytics-based cloud brokering of data protection operations helps enterprises make secure, efficient, and cost-effective use of their cloud infrastructures. Another patented technique — dynamic IT infrastructure optimization in response to cyberthreats — offers unique ransomware analytics and automated optimization that will enable Compass to reconfigure IT infrastructure automatically when it detects cyberthreats, such as a ransomware attack, and dynamically adjust access to backup infrastructure and data to reduce exposure.

Compass is part of IBM’s product portfolio through the IBM Passport Advantage program. Through Passport Advantage, IBM sellers, partners, and distributors around the world can sell Compass under IBM part numbers to any organizations, particularly complex enterprises, that greatly benefit from the automated data protection and anti-ransomware solutions Compass delivers.

CIOReview’s report concludes, “With such innovations, all eyes will be on Cobalt Iron for further advancements in humanless, secure data backup solutions. Cobalt Iron currently focuses on IP protection and continuous R&D to bring about additional cybersecurity-related innovations, promising a more secure future for an enterprise’s data.”

About Cobalt Iron

Cobalt Iron was founded in 2013 to bring about fundamental changes in the world’s approach to secure data protection, and today the company’s Compass ® is the world’s leading SaaS-based enterprise data protection system. Through analytics and automation, Compass enables enterprises to transform and optimize legacy backup solutions into a simple cloud-based architecture with built-in cybersecurity. Processing more than 8 million jobs a month for customers in 44 countries, Compass delivers modern data protection for enterprise customers around the world. www.cobaltiron.com

Product or service names mentioned herein are the trademarks of their respective owners.

Link to Word Doc:www.wallstcom.com/CobaltIron/220728-Cobalt_Iron-CIOReview_Top_IBM_Provider_2022.docx

Photo Link:www.wallstcom.com/CobaltIron/Cobalt_Iron_CIO_Review_Top_IBM_Solution_Provider_Award_Logo.pdf

Photo Caption: Cobalt Iron Compass ® is a SaaS-based data protection platform leveraging strong IBM technologies for delivering a secure, modernized approach to data protection.

Follow Cobalt Iron

https://twitter.com/cobaltiron

https://www.linkedin.com/company/cobalt-iron/

https://www.youtube.com/user/CobaltIronLLC

View source version on businesswire.com:https://www.businesswire.com/news/home/20220728005043/en/

CONTACT: Agency Contact:

Sunny Branson

Wall Street Communications

Tel: +1 801 326 9946

Email:sunny@wallstcom.com

Web:www.wallstcom.comCobalt Iron Contact:

Mary Spurlock

VP of Marketing

Tel: +1 785 979 9461

Email:maspurlock@cobaltiron.com

Web:www.cobaltiron.com

KEYWORD: EUROPE UNITED STATES NORTH AMERICA KANSAS

INDUSTRY KEYWORD: DATA MANAGEMENT SECURITY TECHNOLOGY SOFTWARE NETWORKS INTERNET

SOURCE: Cobalt Iron

Copyright Business Wire 2022.

PUB: 07/28/2022 09:00 AM/DISC: 07/28/2022 09:03 AM

http://www.businesswire.com/news/home/20220728005043/en

Thu, 28 Jul 2022 01:29:00 -0500 en text/html https://www.eagletribune.com/region/cioreview-names-cobalt-iron-among-10-most-promising-ibm-solution-providers-2022/article_56f7dda7-cbd5-586a-9d5f-f882022100da.html
Killexams : Global Digital Twin Market Set To Reach USD 113.3 Billion By 2030, Thriving With A CAGR Of 42.7% | Growth Market Reports

PUNE, India, Aug. 3, 2022 /PRNewswire/ -- According to a exact market study published by Growth Market Reports, titled, "Global Digital Twin Market" by Types (Products, Systems, Processes, and Others), Technologies (IoT & IIoT, Blockchain, Artificial Intelligence & Machine Learning, Big Data Analytics, and Others), Enterprise (Large Enterprises and SMEs), Applications (Product Design & Development, Performance Monitoring, Predictive Maintenance, Inventory Management, Business Optimization, and Others), Industry Verticals (Manufacturing, Automotive, Aerospace & Defense, Energy & Utilities, Oil & Gas, Healthcare, and Others), and Regions: Size, Share, Trends and Opportunity Analysis, 2021-2030", the market is projected to reach USD 113.3 billion in 2030 and is expected to expand at a CAGR of 42.7% during the forecast period. The global digital twin market is projected to expand at a rapid pace, due to the emerging economies, growing population, increasing per capita income, and the rising adoption of digitalization among consumers.

Key Market Players Profiled in the Report

  • GE Digital
  • IBM
  • Microsoft
  • Siemens
  • SAP
  • Oracle
  • ANSYS, Inc
  • Bosch Sicherheitssysteme GmbH
  • Swim Inc
  • Mevea Ltd
  • PTC
  • Rescale, Inc.

The report covers comprehensive data on emerging trends, market drivers, growth opportunities, and restraints that can change the market dynamics of the industry. It provides an in-depth analysis of the market segments which include types, technologies, enterprises, applications, industry verticals, and competitor analysis.

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This report also includes a complete analysis of industry players that covers their latest developments, product portfolio, pricing, mergers, acquisitions, and collaborations. Moreover, it provides crucial strategies that are helping them to expand their market share.

Highlights on the segments of the Digital Twin Market

In terms of types, the global digital twin market is segmented into products, systems, processes, and others. The systems segment is anticipated to expand at a sustainable CAGR during the forecast period, owing to the increasing use of digital twins in various applications by consumers.

Based on technologies, the global digital twin market is segmented into IoT & IIoT, blockchain, artificial intelligence & machine learning, big data analytics, and others. The artificial intelligence & machine learning segment is expected to grow at a significant pace during the forecast period, as digital twin technology is easy to retrain, reuse, and adapt to the existing environment. The AI-enabled digital twin technology offers repeated use and improving functionality by enhancing productivity.

On the basis of enterprises, the global digital twin market is segregated into large enterprises and SMEs. The large enterprise segment is expected to expand at a rapid rate during the forecast period, due to the growing adoption of digital twins by large enterprises for predicting success, maintenance requirements, and failure in enterprises operations.

Based on regions, the market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The market in the Asia Pacific is projected to expand at a significant CAGR during the forecast period, owing to the growth in large-scale industrialization and rising population in the region. The market in North America is anticipated to hold a substantial share of the global digital twin market, due to the increasing deployment of automation solutions by the manufacturing industries.

To Buy the Complete Report: https://growthmarketreports.com/report/digital-twin-market-global-industry-analysis

Key Takeaways from the Study:

  • The digital twin market in North America is expanding and dominating as compared to other regional markets, as the deployment of digital twin aids in improving production lines and downstream operations.
  • The key players in the North America region investing significantly in the digital twin market. North America becomes a major hub for technological innovations. For instance, in August 2021, the General Electric company upgrades its on-premise analytics software, which is Proficy CSense.
  • The market in the Asia Pacific region is attributed to the proliferation of connected devices. Countries including India, China, Australia, and South Korea have a great potential for integrating digital transformation.
  • Automotive segment is anticipated to grow at a significant pace, owing to the rising deployment of automation solutions by the manufacturing industries. The rise in the usage of digital twins for manufacturing, simulation, designing, MRO (maintenance, repair, and overhaul), and after-sales is correlated with the expansion of the automobile market.
  • Key players in the market introducing innovative and advanced products. Companies are encouraged to invest in the R&D of goods and automated processes by the fierce rivalry among large firms.

Read 213 Pages Research Report with Detailed ToC on "Global Digital Twin Market by Types (Products, Systems, Processes, and Others), Technologies (IoT & IIoT, Blockchain, Artificial Intelligence & Machine Learning, Big Data Analytics, and Others), Enterprise (Large Enterprises and SMEs), Applications (Product Design & Development, Performance Monitoring, Predictive Maintenance, Inventory Management, Business Optimization, and Others), Industry Verticals (Manufacturing, Automotive, Aerospace & Defense, Energy & Utilities, Oil & Gas, Healthcare, and Others), and Regions (North America, Latin America, Europe, Asia Pacific, and Middle East & Africa) - Global Industry Analysis, Growth, Share, Size, Trends, and Forecast 2021 - 2030"

For Any Questions on This Report: https://growthmarketreports.com/enquiry-before-buying/3853

Key Segments Covered

Types

  • Products
  • Systems
  • Processes
  • Others

Technologies

  • IoT & IIoT
  • Blockchain
  • Artificial Intelligence & Machine Learning
  • Big Data Analytics
  • Others

Enterprise

Applications

  • Product Design & Development
  • Performance Monitoring
  • Predictive Maintenance
  • Inventory Management
  • Business Optimization
  • Others

Industry Verticals

  • Manufacturing
  • Automotive
  • Aerospace & Defense
  • Energy & Utilities
  • Oil & Gas
  • Healthcare
  • Others

By Regions

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • Middle East & Africa (MEA)

Other Related Reports:

  • Global Internet of Things IoT Controllers Market by Type (Wi-Fi IoT Controllers, Bluetooth IoT Controllers, ZigBee IoT Controllers, Other), By Application (Home Appliance, HVAC Monitoring, Fire/Gas/Leak Detection, Remote Controls, Other) And By Region (North America, Latin America, Europe, Asia Pacific and Middle East & Africa), Forecast From 2022 To 2030
  • Global Industrial Predictive Maintenance Market by Type (Cloud-Based, On-premises), By Application (Government, Aerospace and Defense, Energy and Utilities, Healthcare, Manufacturing, Transportation and Logistics) and Region (North America, Latin America, Europe, Asia Pacific and Middle East & Africa), Forecast To 2028
  • Global Artificial Intelligence in Video Games Market by Type (On-Premise Artificial Intelligence in Video Games, Cloud-based Artificial Intelligence in Video Games), By Application (PC, TV, Smartphone & Tablet) and Region (North America, Latin America, Europe, Asia Pacific and Middle East & Africa), Forecast From 2022 To 2030
  • Smart Manufacturing Market by Technology (SCADA, Programmable Logic Controller, Machine Vision, Machine Execution Systems, Enterprise Resource Planning, Product Lifecycle Management, Human Machine Interface, 3D Printing, Distributed Control Systems, and Plant Asset Management), Components (Services, Software, and Hardware), End-users (Oil & Gas, Healthcare, Aerospace & Defense, Automotive, Chemicals & Materials, Industrial Equipment, Electronics, Food & Agriculture, and Others), and Regions (Asia Pacific, North America, Latin America, Europe, and Middle East & Africa) - Global Industry Analysis, Growth, Share, Size, Trends, and Forecast 2021 - 2028

About Growth Market Reports:

Growth Market Reports provides global enterprises as well as medium and small businesses with unmatched quality "Market Research Reports" and "Industry Intelligence Solutions". Growth Market Reports has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domains.

Our key analysis segments, though not restricted to the same, include market entry strategies, market size estimations, market trend analysis, market opportunity analysis, market threat analysis, market growth/fall forecasting, primary interviews, and secondary research & consumer surveys.

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Killexams : AI in Computer Vision Market Report Covers Future Trends with Research 2022-2030 | NVIDIA, Intel Corp., Microsoft Corp., IBM Corp., Qualcomm, AWS

AI in Computer Vision market report provides a detailed study of global market scope, regional and country-level market size, segmentation, growth, share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, exact developments, opportunities analysis, strategic market growth analysis, product launches and technological innovations.

Artificial intelligence (AI) refers to a computer’s or a computer-enabled robotic system’s ability to organize information and create outcomes in learning, decision-making, and problem-solving that are similar to human brain processes. Computer vision is a branch of computer science dedicated to simulating the complexity of the human visual system and allowing computers to recognize and analyze things in photos and videos in the same way that people do. The massive amount of data generated in everyday life is a primary driving force behind the advancement of computer vision.

Market segmentation
AI in Computer Vision market is divided by Type and Application. For the period 2022-2030, the growth among segments provides accurate calculations and forecasts for revenue by Type and Application. This analysis can help you expand your business by targeting qualified place market

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AI in Computer Vision Market, By Type:

AI in Computer Vision Market, By Application

  • Non-industrial
  • Industrial

Market segment by players, this report covers

NVIDIA, Intel Corp., Microsoft Corp., IBM Corp., Qualcomm, AWS, Xilinx, Google, Facebook, Basler.

Market segment by regions, regional analysis covers

  • North America
  • Europe
  • Asia Pacific
  • Latin America
  • Middle East & Africa

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Years Considered for the AI in Computer Vision Market Size:

  • Historic Years: 2015-2020
  • Base Year: 2021
  • Forecast Years: 2022-2030

Reasons to Purchase the AI in Computer Vision Market Report:

  • The report includes an excess of information such as market dynamics scenario and opportunities during the forecast period.
  • Segments and sub-segments include quantitative, qualitative, value and volume data.
  • Regional, sub-regional and country level data includes the demand and supply forces along with their impact on the market.
  • The competitive landscape covers share of key players, new growths and strategies.
  • All-inclusive companies offering products, relevant financial information, exact developments, SWOT analysis, and strategies by these players.

The report covers exhaustive analysis on:

  • AI in Computer Vision Market segments
  • AI in Computer Vision Market dynamics
  • Environment analysis
  • AI in Computer Vision Market current trends/issues/challenges
  • Competition & Companies involved technology
  • Value Chain
  • AI in Computer Vision Market drivers, restraints and opportunities

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Killexams : Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

The MarketWatch News Department was not involved in the creation of this content.

Jul 25, 2022 (AB Digital via COMTEX) -- The Quantum Computing Software Market size is projected to grow from USD 0.11 billion in 2021 to 0.43 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period. The major factors driving the growth of the Quantum Computing Software market include the growing adoption of quantum computing software in the BFSI vertical, government support for the development and deployment of the technology, and the increasing number of strategic alliances for research and development.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=179309719

Based on Component, the service segment to grow at a higher CAGR during the forecast period

Among the component segment, the services segment is leading the quantum computing software market in 2021. The growth of the services segment can be attributed to the increasing investments by start-ups in research and development related to quantum computing technology. Quantum computing software and services are used in optimization, simulation, and machine learning applications, thereby leading to optimum utilization costs and highly efficient operations in various industries.

Based on application, the optimization segment is expected to hold the highest market size during the forecast period

The optimization segment is expected to lead the global quantum computing software market in terms of market share. Optimization problems exist across all industries and business functions. Some of these problems take too long to be solved optimally with traditional computers, where the usage of quantum computing technology is expected to be an optimum solution. Several optimization problems require a global minimal point solution. By using quantum annealing, the optimization problems can be solved earlier as compared to supercomputers.

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Major Quantum Computing Software vendors include IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK), Zapata Computing (US), Quantum Circuits, Inc. (US), Quantica Computacao (India), XANADU Quantum Technologies (Canada), VeriQloud (France), Quantastica (Finland), AVANETIX (Germany), Kuano (England), Rahko (UK), Ketita Labs (Estonia), and Aliro Quantum (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

IBM was founded in 1911 and is headquartered in New York, US. It is a multinational technology and consulting corporation that offers infrastructure, hosting, and consulting services. The company operates through five major business segments: Cloud and Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. IBM Cloud has emerged as a platform of choice for all business applications, as it is AI compatible. It is a unifying platform that integrates IBM’s capabilities with a single architecture and spans over public and private cloud platforms. With this powerful cloud platform, the company can cater to the requirements of different businesses across the globe. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining. The company has a strong presence in the Americas, Europe, MEA, and APAC and clients in more than 175 countries. IBM is one of the major players in the quantum computing ecosystem. The company in 2016 made a quantum computer available to the public by connecting it to the cloud. In September 2019, it opened a Quantum Computation Center. The Quantum Computation Center offers about 100 IBM clients, academic institutions, and more than 200,000 registered users access to this cutting-edge technology through a collaborative effort called the IBM Q Network and Qiskit, IBM’s open-source development platform for quantum computing. Through these efforts, IBM is exploring the ways quantum computing can address the most complicated problems faced while training the workforce to use this technology.

Rigetti Computing was founded in 2013 and is headquartered in California, US. Rigetti Computing designs and manufactures superconducting quantum-integrated circuits. It develops quantum computers, as well as superconducting quantum processors that power them. The machines of the company can be integrated with any public, private, or hybrid cloud through the quantum cloud services (QCS) platform. It is a full-stack quantum computing company that provides an integrated computing environment. Rigetti Computing develops algorithms for quantum computing that focus on application areas such as machine learning, logistics, healthcare and pharmaceuticals, and chemicals. The company also delivers a set of tools, such as Quil, pyQuil, and Quilc, which help solve optimization problems.

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