Introduction

The MLOps Foundation Certification is a comprehensive program designed for professionals who want to excel in the integration of Machine Learning (ML) operations with DevOps practices. This certification, introduced by DevOpsSchool in association with expert trainer Rajesh Kumar from www.RajeshKumar.xyz, aims to provide participants with essential knowledge and skills to deploy and manage machine learning models effectively in a production environment.

Why MLOps?

MLOps, a combination of Machine Learning and Operations (DevOps), is critical for organizations that leverage AI and ML models in production. It bridges the gap between data scientists and operations teams, enabling continuous integration and continuous delivery (CI/CD) of machine learning models. This ensures rapid deployment, monitoring, and scalability of models, leading to robust and efficient AI-driven solutions.

Who Should Attend?

The MLOps Foundation Certification is ideal for:

• Data Scientists looking to understand the deployment of ML models in production.
• DevOps Engineers who want to add ML model management to their skill set.
• Software Engineers and Developers interested in the field of AI/ML.
• IT Professionals who are responsible for managing ML projects.
• Anyone eager to learn the fundamentals of MLOps and its implementation in real-world scenarios.

Key Benefits of the Certification

• Comprehensive Learning: Gain in-depth knowledge of MLOps principles, tools, and practices.
• Expert Guidance: Learn from Rajesh Kumar, an industry expert with extensive experience in DevOps and MLOps.
• Hands-On Experience: Work on real-world projects to understand the practical aspects of MLOps.
• Career Advancement: Enhance your resume with a certification recognized by top companies in the tech industry.

Prerequisites

• Basic knowledge of Machine Learning concepts.
• Familiarity with DevOps practices and tools.
• Understanding of Python programming language.
• Experience with cloud platforms (AWS, Azure, or Google Cloud) is a plus.

Course Agenda

The MLOps Foundation Certification program is structured to cover all essential aspects of MLOps:

Certification Exam Details

• Format: Multiple-choice questions + Hands-on project submission
• Duration: 2 hours for the exam
• Passing Score: 70%
• Project Evaluation: Based on the hands-on project submission

Trainer Profile: Rajesh Kumar

Rajesh Kumar is a renowned trainer and expert in the field of DevOps, with years of experience in delivering practical knowledge across various DevOps tools and methodologies. With a strong background in cloud computing and machine learning, Rajesh brings a wealth of expertise, making this MLOps Foundation Certification a highly valuable learning experience.

How to Enroll

• Visit the official DevOpsSchool website.
• Choose the “MLOps Foundation Certification” course and complete the registration.
• Start your journey toward mastering MLOps under the guidance of Rajesh Kumar.

Conclusion

The MLOps Foundation Certification is an essential course for anyone looking to master the integration of Machine Learning and DevOps. With hands-on projects, expert guidance, and real-world case studies, this certification will equip you with the skills necessary to succeed in the field of MLOps.

The post MLOps Foundation Certification appeared first on Artificial Intelligence.

Big data is nothing but the confidential information storage center, which needs the support of AI to manage the huge volumes and function seamlessly.

In most organizations, big data storage is spread across different computers, on-prem, or on the cloud. The analysis of such an incredible and useful source of data, is best done with AI based tools and applications.

The insights derived by AI tools from both structured and unstructured data, effectively have the power to change the course of a company’s business growth. The World Economic Forum in 2016 has estimated an increase of stupendous US$100 trillion in social value and global business by 2030. Most of this growth will be driven by the power of data collated today!

A report suggests that, on average, every day, humans create about 2.5 quintillion bytes of data. If all these data are well-utilized, they will surely allow humans to get an improved view of the future. In business, this could be the difference between survival, or liquidation- or explosive growth.  To be precise, data is valued to assets, which allows companies to get a hint of the future through advanced analysis.

AI technologies will probably be one of the biggets drivers for this growth, if studies from  PwC and McKinsey are anything to go by. They estimate the increase in AI tools business to touch US$15.7 trillion, and be around US$13 trillion of the annual GDP by 2030. However, there are a few significant challenges that companies face concerning big data, which could be easily resolved by adopting technological changes:

Diversity in IT source system

Storing data is always a complicated process, and securing, maintaining/managing it is even more difficult. The average Fortune 500 Company has thousands of enterprise IT systems across diverse formats, with mismatched references across data sources and duplication errors. Such diversity only complicates the situation and creates chaos.

High-frequency data management

Data flow is real-time, so critical issues like censoring of data still stay unspoken. So, high-frequency data management not only complicates the process but multiplies the vulnerabilities and risks. AI can help sort and censor the data as and when they flow in.

Organizing data content from diverse sources

Since big data is gathered from varying and wide varieties of sources, their formats are different, and most of it is unstructured.  So, even to structure the data into analyze-able bites is challenging and involves a lot of tools. This is merely to differentiate them and put them across diverse channels before conducting the in-depth analysis. One more added issue is data clarity, as some files don’t even comply with the set minimum clarity bar.

Looking at all these issues, AI can be the superhero sorting them all. Enterprise analytics and IT team need to provide advanced tools to empower employees with diverse levels of data science proficiency to function effectively with large data sets, and perform smart predictive analytics using a unified vision.

 Resolving the big data issues

Data Scientists could potentially be the magicians that will derive insights form the humongous amounts of Big Data in the market today. The predictions and insights could be the deciding factor for nay industry. Many CIOs feel ML algorithms are of great use as it facilitates the necessity to receive new data, generate outcomes, and have some decisions or actions to be made based on the outputs. Multiple algorithm libraries are widely available to data scientists today, and they can use these to create the highest quality actionable insights.

Big data issues like unstructured data, bad clarity content, data lakes, etc. are problems that could be demystified with the help of ML, data science applications, and with efficient application of AI tools.

The post Nurturing Big Data with the Power of Artificial Intelligence appeared first on Artificial Intelligence.

Veeam Software, the leader in Backup solutions that deliver Cloud Data Management, announced it has been positioned by Gartner, Inc. in the Leaders quadrant of the 2020 Magic Quadrant for Data Center Backup and Recovery Solutions1. Not only does this mark the fourth time Gartner has recognized Veeam as a category Leader, but it is the first time Veeam is positioned highest overall in ability to execute. Veeam was also the only vendor to move higher in both ability to execute and in the completeness of vision categories. We believe this recognition further validates Veeam’s investment in delivering a complete Cloud Data Management portfolio to its customers and partners, fused with robust support that ensures data protection across physical, virtual and cloud environments.

“To us, being named a Leader by Gartner for the fourth time cements our commitment to lead the industry with innovation, execution, and delivering the most simple, flexible and reliable solutions for Cloud Data Management,” said Danny Allan, CTO and Senior Vice President of Product Strategy at Veeam. “With more than 375,000 customers and $1B in annual bookings, we continue to guide our customers through their digital transformation and Hybrid/Multi-cloud journeys at a time when data protection is paramount and leveraging data reuse for overall business value is critical.”

Veeam released Veeam Availability Suite (VAS) v10 earlier this year, delivering modern file data protection for Networked Attached Storage (NAS), Multi-VM Instant Recovery™ to automate disaster recovery (DR), and heightened ransomware protection. With greater platform extensibility, data mining through APIs, and more than 150 major enhancements, Veeam has launched the industry’s most robust solution for complete data management and protection for hybrid-cloud environments.

The report included analysis of 11 data center backup and recovery solutions vendors. We think now is a time when the move toward public cloud, heightened concerns over ransomware, and complexities associated with backup and data management are forcing I&O leaders to rearchitect their backup infrastructure and explore alternative solutions.

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DDN, premier provider of Artificial Intelligence (AI) and Data Management software and hardware solutions enabling Intelligent Infrastructure, unveiled its new corporate brand identity, including a new logo and refreshed website and informational materials. This dramatic revolution to the brand underscores the new company vision and direction to deliver best-in-class technology to the broader enterprise market that DDN’s current AI and big data customers trust with their crucial and ever-growing data.

“Over the last two decades, DDN has developed a strong reputation and a significant global footprint in the areas of AI, big data, multicloud, and high-performance computing. Our customers’ sustained trust in us and our products has made us the largest privately held storage company,” said Alex Bouzari, CEO and co-founder, DDN. “With our recent acquisitions and continued investment in innovation, we have the technology and expertise to optimize business operations with Intelligent Infrastructure for a changing world.”

In 2019, DDN acquired the IntelliFlash business from Western Digital Corp., as well as Nexenta, the market leader in Software Defined Storage (SDS) for 5G and Internet of Things (IoT). These companies joined Tintri®, acquired in 2018, to deliver a different enterprise customer experience through the utmost flexibility, speed at any scale and data insight for on-premise and multicloud environments, powered by Intelligent Infrastructure innovations that advance real-time and predictive application analytics. IntelliFlash, Nexenta and Tintri, have all been consolidated under the Tintri brand to spearhead its product and service efforts globally in the enterprise space.

With more than 10,000 customers, 20 exabytes of value-add storage solutions delivered to many of the most demanding datacentric companies, government and research facilities in the world, 1,000 DDN employees predominantly focused on R&D and customer-facing technical areas, and more than 140 patents, DDN continues to push the boundaries of innovation to constantly exceed customer requirements.

“Our data is our company, so we needed a robust storage architecture to support our AI-driven models. Managing our at-scale data needs required faster ingest, optimized processing and reduced application run times,” said Kris Howard, principal systems engineer, at Recursion, a digital biology company industrializing drug discovery. “We wanted to change the drug discovery paradigm, and we knew that storage played a big part in that. DDN tailored an ideal configuration for our performance and budget requirements, and then helped sell the concept to upper management on how it works—which was just what we needed.”

“DDN has quickly evolved from an HPC storage provider to an award-winning technology solution provider that serves the needs of any customer that puts a premium on its data,” said Kurt Kuckein, vice president, marketing, DDN. “Our new logo, design aesthetic and corporate messaging reflects this broader market direction, delivering a consistent approach that encompasses the technologies and companies we’ve acquired and opens the door to future opportunities that support our business strategy.”

DDN’s new circular segmented logo symbolizes the company’s new energy and reflects its path of continuous innovation and renewal. A bright and vibrant new color scheme pays tribute to its legacy and modernizes the brand to underscore the company’s dedication to its customers and the desire to combine the best technologies with dynamic expertise to deliver a streamlined experience with deeper more valuable insight into their data assets.

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Cloudera today announced that it plans to make an instance of its data management platform based on Hadoop generally available this summer on Red Hat OpenShift, which is based on Kubernetes.

Arun Murthy, chief product officer for Cloudera, says Cloudera Data Platform (CDP) Private Cloud is a complement to the instances of the platform already available on Amazon Web Services (AWS) and Microsoft.

The goal is to enable IT teams to deploy a data warehouse based on CDP in the cloud or on-premises IT environments and move data across a hybrid cloud computing environment, says Murthy.

Thanks to the rise of Kubernetes it’s now easier to move workloads between cloud computing environments. However, moving data between cloud platforms has been more problematic. CDP simplifies the movement of data between cloud platforms, enabling IT teams to preserve metadata as well as the relevant security and governance controls that should be maintained, notes Murthy.

That’s critical because in the wake of the economic downturn brought on by the COVID-19 pandemic, many IT organizations are looking to centralize the management of multiple clouds to reduce the total cost, he adds.

Murthy says Cloudera will add support for other distributions of Kubernetes based on demand, noting Red Hat OpenShift currently is the dominant distribution of Kubernetes being deployed in on-premises IT environments.

CDP is based on two distributions of Hadoop coming together as a result of the Cloudera-Hortonworks merger at the beginning of last year. Since then, Hadoop and Kubernetes have played key roles in driving development of artificial intelligence applications incorporating machine and deep learning algorithms. Hadoop provides a means to manage massive amounts of data, while the containers orchestrated by Kubernetes make it possible to employ microservices to build and deploy what would otherwise be an unwieldy monolithic AI application.

Of course, as the amount of data being aggregated reaches into the petabytes, the term “big data” has become somewhat passé. The issue is not so much the amount of data being stored and processes as much as it is making sure the right data is being made available to the right microservice at the right time. In effect, sets of data need to be managed as a logical entity that can be accessed by multiple microservices, notes Murthy.

Cloudera, in fact, already makes available separate data warehouse, machine learning and data management and analytics services on top of CDP to simplify the management of data within the context of specific use cases.

It may be a while before IT teams master all the nuances of data management in a hybrid cloud computing era enabled by Kubernetes. However, as organizations seek to derive more value from the data they collect, they will need more flexible approaches to managing massive amounts of data. With the rise of agile development methodologies and DevOps, it’s never been easier to create and deploy an application. By comparison, giving those applications access to the data they require remains positively glacial in far too many organizations.

The post Cloudera’s Data Management Platform Comes to OpenShift appeared first on Artificial Intelligence.

AI (ML) has been utilized for a long time in different industries to drive new business, increase productivity, reduce risk and improve consumer satisfaction. However, within data management, widespread adoption still can’t seem to progress. One issue is that use cases and capacities of ML related to data management are not constantly comprehended by operational teams.

Another is that the undeniable use cases require high levels of accuracy, while the accuracy of ML methods is as of now observed as hard to anticipate. Above all, there is a strong everyday spotlight on delivering cleansed data to downstream applications, for example, risk, trade support, and compliance engines, leaving little time to improve or set out on apparent, large undertakings.

There are numerous potential use cases of ML in data management, in any case, that can lessen operational cost through improved efficiency, a superior user experience through context-driven user interfaces, reduced risk, and improved services and data quality through increasingly viable operations.

As indicated by Gartner, “Within the following year, the number of data and analytics experts in business units will develop at multiple times the pace of specialists in IT divisions, which will compel organizations to reexamine their authoritative models and ranges of abilities.” We believe, so the demand for usable enterprise data is outstripping supply. To deliver spotless, unified and business-ready data at scale, data leaders should change the manner in which they work. Unmistakably, something has to give. Furthermore, it is.

With advances in machine learning, cloud computing and storage, enterprises are finally breaking the data-management logjam. In question are breakout upgrades in business proficiency, revenue realization, product innovation and competitive differentiation. The outcomes driven here could be transformational.

For CIOs and CISOs stressed over security, compliance and scheduling SLAs, it’s basic to understand that ever-expanding volumes and varieties of data, it’s not humanly workable for an administrator or even a team of administrators and data scientists to tackle these challenges. Luckily, machine learning can help.

A variety of machine learning and deep learning strategies might be utilized to achieve this. Comprehensively, machine/deep learning methods might be named either unsupervised learning, supervised learning, or reinforcement learning

The decision of which strategy will be driven by what issue is being fathomed. For instance, supervised learning mechanisms, for example, random forest might be utilized to build up a gauge, or what comprises “typical” behavior for a system, by observing applicable traits, at that point utilize the benchmark to identify inconsistencies that stray from the standard. Such a framework could be utilized to detect security threats to the framework. This is particularly important for recognizing ransomware attacks that are slow advancing in nature and don’t encrypt information at the same time but instead bit by bit after some time. Random forest (just as Gradient Boosted Tree) methods could likewise be utilized to tackle the previously mentioned workflow scheduling problem by modeling the system load and resource availability metrics as training characteristics and from that model decides the best occasions to run certain occupations.

Nonetheless, in many cases, the underlying training information utilized in model creation will be unlabeled, in this manner rendering supervised learning techniques useless. While unsupervised learning may appear to be a characteristic fit, an elective methodology that could bring about increasingly exact models includes a pre-processing step to assign labels to unlabeled data such that makes it usable for supervised learning.

Within control frameworks, ML can help lessen the expense of checking huge data volumes through performant big data analytics, increment the viability of controls by using deep learning strategies and improve compliance with approaches utilizing ML algorithms that process unstructured data and find processes and anomalous user activity from work performed. The advantage of beginning with data risks and controls is that every one of these upgrades can be made with little investment and without affecting the Business-As-Usual exercises.

One use case where ML increases the value of key controls is exception handling. This is maybe the most significant control in data management. Its key ability of convenient and exact information checks helps discover irregularities which in this manner require validation by a data cleanser. Exception handling can only be effective if the correct guidelines are applied to data objects.

The consistent application of checks over the information universe, particularly within an enormous universe, can be hard to evaluate and this is the place ML (for example anomaly detection) can have any kind of effect by recognizing data objects that are not appropriately checked with the goal that operational users can relegate the proper guidelines and improve the viability of the exception handling control.

With the correct use cases, data management teams can, with little investment rapidly experience the advantages that ML brings. Cost can be kept low as analytical libraries are generally available and ML skill is increasingly broad in various enterprises, including financial services. By utilizing new analytics, data management productivity will increase, controls will improve, risks will reduce, and data quality will increase, while making a significant stride in planning for stricter data quality guidelines, for example in which information quality systems are required, where data risks need to be identified, monitored and controlled, and controls need to be regularly evaluated for effectiveness and improved upon.

The post MACHINE LEARNING PLAYING AN IMPORTANT ROLE IN DATA MANAGEMENT appeared first on Artificial Intelligence.

Oracle’s Data Science is helping retailers find their top future customers using data science.

A new offering from Oracle Retail, Consumer Insights aids retailers in understanding the characteristics of their best customers then extends those traits to find similar potential customers among the petabytes of third-party consumer data in Oracle Data Cloud. This enables retailers to optimize customer acquisition campaigns with more relevant, targeted products and offers.

Oracle Data Cloud combines the leading technologies and talent from Oracle’s acquisitions of AddThis, BlueKai, Crosswise, Datalogix, Grapeshot, and Moat.

Gaining new customers is a top priority for retail marketers, with the cost growing every year. In the recent holiday season, a new survey showed that 77 percent of retailers planned to increase their spend in this area. But are they reaching the right prospective buyers?

With Oracle Retail Consumer Insights, retailers can achieve a deep understanding of their existing customers through enriched attributes and advanced data science. For example, do the most profitable athletic gear customers also purchase particular brands of soft drinks or deodorant or snack foods? Is there a common denominator in the type of vehicle they drive or restaurants they frequent or vacations they take?

By enriching first-party data retailers have on their existing customers with purchase data and other characteristics that happen outside the retailer’s vantage point, Consumer Insights can cluster together attributes and actions and identify new segments that would be otherwise unknown. Retailers can then use this information to find similar buyers to target with offers that are highly relevant to their lifestyle and tastes.

The data in Oracle Data Cloud represents profile-linked transaction-level sales data and a rich set of other demographic, geographic, and interest attributes from Oracle Data Cloud. Through this new solution, that third-party data can now be coupled with first-party data retailers have on customers, omnichannel touch-points, inventory movements, promotional response, and much more.

“The value of data can’t be found in zeros and ones, but in human connections to the interests, experiences, and behavior of current and potential customers,” said Cecilia Mao, vice president of product, Oracle Data Cloud. “When you know that your customers are also more likely to buy at the grocery store, brand affinity and hobbies, you can build more accurate models to find your best potential customers, then use multiple channels to reach them at scale.”

Applying predictive and prescriptive analytics to this data, retailers can connect, analyze, experiment, and explore new customer segments, knowing that advanced decision science is under the hood. Consumer Insights evaluates “what if” analysis and explores and finds prospects in a continuously iterative process to get the criteria right and identify the most appropriate customer segment. Once correctly identified, retailers can take action by launching campaigns, promotions, or advertising, with the option to activate using Oracle Data Cloud’s industry-wide connections.

“When it comes to grabbing the attention of potential customers, every second and moment matters,” said Jeff Warren, Vice President, Oracle Retail.

Jeff added, “Armed with intelligent data based on real customer attributes and behaviors, retailers will truly be able to put the needs and likes of shoppers at the center of their new customer acquisition strategy.”

Visit Oracle (booth #4837) at NRF 2020 Vision Retail’s Big Show (January 12-14) at Jacob K. Javits Convention Center, New York City, to see Oracle Retail Consumer Insights live.

While in the booth, see the full Oracle Retail Insights Suite and demo the new Retail Home dashboards to see why Oracle is the modern platform for retail.

Oracle is the modern platform for retail.

Oracle provides retailers with a complete, open, and integrated platform for best-of-breed business applications, cloud services, and hardware that are engineered to work together. Leading fashion, grocery, and specialty retailers use Oracle solutions to accelerate from best practice to next practice, drive operational agility, and refine the customer experience.

Currently, Oracle Data Cloud helps marketers use data to capture consumer attention and drive results. Used by 199 of AdAge’s 200 largest advertisers, our Audience, Context and Measurement solutions extend across the top media platforms and a global footprint of more than 100 countries. We give marketers the data and tools needed for every stage of the marketing journey, from audience planning to pre-bid brand safety, contextual relevance, viewability confirmation, fraud protection, and ROI measurement.

The post Oracle Retail’s Data Science Empowers Retailers to Find Future Top Customers appeared first on Artificial Intelligence.

Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT).

Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation.

5G

Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G). Compared with the mobile foundation that is presently set up, 5G will bring higher throughput, lower latency, progressively effective signaling, support for more range groups, greater programmability and other extra advanced procedures to expand utilization and optimize costs. The number of connected gadgets will significantly increase because of this improved performance: sensors will profit by progressively affordable bandwidth to the internet; heavy users of uplink traffic like video cameras will have the option to share more information; fast-moving gadgets (drones) will have increasingly solid connectivity, etc. These new gadgets will be the impetus of another wave of development for every single included industry.

Virtual Stylist

A few retailers are as of now directing AI/ML-based tools that perceive clients’ appearances and dress to make suggestions. In Hong Kong, fashion retailer Guess opened a pilot FashionAI idea shop at Hong Kong Polytechnic University. At the idea shop, machine learning and computer vision are deployed to “learn” from purchasers and designers inside the framework. Customers looked into the store with facial recognition innovation. RFID-empowered dress rack alternatives consequently appeared on the smart mirror, which offered styling recommendations.

Other AI/ML-based styling assistants give the data to sales associates so they can personally furnish clients with suggestions, making the shopping procedure progressively consistent and effective.

Intelligent Transportation Systems

Advances in Intelligent Transportation Systems (ITS) are prompting the introduction of an ever increasing number of vehicles with autonomous driving abilities. Notwithstanding, intelligent automation in ITS isn’t constrained to autonomous vehicles alone. There are endeavors in progress to build the effectiveness of traffic systems at a vital level, for example, the structure of streets and relics (signal lights, traffic islands, bus stops, vehicle parking, etc), the control of traffic signals and the setup of directions dependent on mobility pattern predictions.

Every one of these applications require the processing of tremendous amounts of information to remove the necessary information and settle on worldwide decisions. Tighter control loops at the strategic level incorporate working and coordinating traffic lights for maximal throughput, and taking care of traffic congestion because of unexpected occasions, for example, mishaps. Much of the time, despite the fact that the events appear to require just local intervention, without a worldwide perspective, a neighborhood activity can prompt gridlock in a bigger zone. It is surely known that machine learning is relevant to practically all tasks across numerous sectors and can accomplish effectiveness through smart and adaptive automation.

Smart Agents Technology

Smart Agents innovation is a personalization innovation that makes a virtual portrayal of each entity and learns/builds a profile from the entity’s actions and activities. In the payment business, for instance, a Smart Agent is related with every individual cardholder, dealer, or terminal. The Smart Agent related to an entity, (for example, a card or merchant) learns in real-time from each transaction made and constructs their particular and remarkable practices after some time. There are the same number of Smart Agents as dynamic elements in the framework. For instance, if there are 200 million cards executing, there will be 200 million Smart Agents started up to dissect and learn the behavior of each.

Decision-making is explicit to every cardholder and never again depends on rationale that is all around applied to all cardholders, paying little respect to their individual attributes. The Smart Agents are self-learning and versatile since they ceaselessly update their individual profiles from every movement and activity performed by the entity. Each Smart Agent pulls every single important data over different channels, regardless of the sort of configuration and source of the information, to deliver virtual profiles.

Master Data Management

Data management and duplicate data entries have consistently been a struggle for organizations all things considered. A database with 30 million clients may in reality just be 3,000,000 one of a kind users. This is an issue that has caused database managers endless cerebral pains, and it harms the bottom line for organizations.

Utilizing AI/ML innovation, organizations can actualize an all-in-one server add-on that runs flawlessly in the background, examining and analyzing user entries in real time. It can even be designed to block duplicate user sign-ins as they happen. The AI/ML solution does this via matching user data and comparing data, for example, username, email, telephone number, address, Social Security numbers, linked credit cards, IP information and much more. There is no compelling reason to run custom inquiries or reports, sparing time and human capital.

These are only a couple of instances of the manner in which the world keeps on embracing AI and ML innovation to improve the manner in which we live. The companies that embrace these cutting-edge applications will work all the more effectively, give their clients better experiences and lead their enterprises. Business pioneers should ensure their companies don’t get left behind.

The post Leading-Edge Applications of AI and Machine Learning appeared first on Artificial Intelligence.

Hortonworks, IBM and Red Hat today announced they’re banding together to build a consistent hybrid computing architecture for big data workloads. Dubbed the Open Hybrid Architecture Initiative, the program pledges simplicity of deployment and freedom of movement for data apps.

The rapid ascent of cloud computing platforms like AWS, Azure, and Google Cloud has given enterprises abundant new options for storing data and deploying processing-intensive applications, such as deep learning and real-time stream processing. Throw in the progress being made at the edge, with sensors and speedy ARM chips collecting and processing massive amounts of data, and you have the makings of a computing revolution.

While the computing possibilities in the cloud and on the edge may appear bountiful, the reality is that the underlying architectures for building apps that can span these three modes are just starting to come together. Enterprises today face a dearth of repeatable patterns to guide their developers, administrators, and architects, who are tasked with building, deploying and maintaining hybrid that span not just the cloud and the edge, but traditional on-prem data centers too.

That’s the underlying challenge that’s to be faced by the Open Hybrid Architecture Initiative. Launched today in advanced of the Strata Data Conference in New York City, the group outlined plans to integrate their respective technologies in such a way as to provide customers with greater freedom of movement and run-time options for their big data workloads.

In terms of actual deliverables, the initial phase of the initiative calls for the companies to integrate various products. Specifically, HortonworksData Platform (HDP), Hortonworks DataFlow (HDF), Hortonworks DataPlane (DPS) and IBMCloud Private for Data will be optimized to run on Red Hat OpenShift, the company’s distribution of Kubernetes for containerized applications.

The companies say the move will “provide the vast OpenShift community of developers and users – which include IBM and Hortonworks clients – fast access to robust analytics, data science, machine learning, data management and governance capabilities, fully supported across hybrid clouds.”

Enterprises will be able to access and process data no matter where it resides as part of the Open Hybrid Architecture Initiative, says Hortonworks co-founder and CTO Arun Murthy.

“Through the initiative, we deliver an architecture where it absolutely will not matter where your data is – in any cloud, on-prem or the edge – enterprises can leverage open-source analytics in a secure and governed manner,” he says in a blog post today. “The benefits of ensuring a consistent interaction model cannot be overstated and provides the key to unlocking a seamless experience.”

Kubernetes stands to play a starring role in the Open Hybrid Architecture Initiative. The open source container management software serves as a virtualization layer that decouples runtime environments from underlying hardware, while providing the capability to spin up, spin down, and move software applications at the administrator’s will.

“By building and managing their applications via containers and Kubernetes with OpenShift,” says Ashesh Badani, vice president and general manager of Cloud Platforms at Red Hat, “customers and the big data ecosystem have opportunities to bring this next generation of big data workloads to the hybrid cloud and deliver the benefits of an agile, efficient, reliable, multi-cloud infrastructure.”

IBM is currently working on achieving “primed” status for running IBM Cloud Private for Data on OpenShift, which is expected to occur later this month. “Scaling the ladder to AI demands robust data prep, analytics, data science and governance, all of which are easily scaled and streamlined in the kind of containerized, Kubernetes-orchestrated environments that we’re talking about today,” says Rob Thomas, general manager of IBM Analytics.

Hortonworks is following a similar path with its products, including DPS, which it launched a year ago and which will be called upon for spinning various engines like Hive and Spark up and down in a hybrid architecture while maintaining necessary controls that enterprises demand. “This allows customers to more easily adopt a hybrid architecture for big data applications and analytics, all with the common and trusted security, data governance and operations that enterprises require,” Murthy says.

It’s not clear if any cloud providers are working with the Open Hybrid Architecture Initiative at this point, or if there are any other members of the group. There is no website set up yet for the Open Hybrid Architecture Initiative, but a spokesperson for Hortonworks says there will be one soon.

The Hortonworks spokesperson says cloud platform vendors are welcome to join the group.  “We welcome participation from anyone who wants to collaborate to accelerate hybrid for customers,” the spokesperson says.

In any event, it’s not all about moving applications out of the data center and into the cloud. According to a recent IDC survey, more than 80% of respondents said they plan to move or repatriate data and workloads from public cloud environments behind the firewall to hosted private clouds or on-premises locations over the next year.

The reason for this “application repatriation” is that the “initial expectations of a single public cloud provider were not realized,” the group says.

The post ‘Open Hybrid’ Initiative Targets Big Data Workloads appeared first on Artificial Intelligence.

For most businesses today, data management has shifted from an important competency to a critical differentiator and determines industry winners and has-beens.

Government bodies and Fortune 1000 companies benefit from the innovations of web developers. These organizations are reevaluating existing strategies and defining new initiatives to transform their businesses using “big data”. These trends indicate that big data is not a single technology or initiative. It is, rather, a revolution across many areas of technology and business.

Big data plays an important role in many different industries across the world. To make the most out of it, you can consider ensure your employees get trained in big data. When they learn about proper management of big data, your business will become productive and will improve in efficiency.

Big Data refers to a collection of data sets, so massive and complex that it becomes difficult to process with traditional applications/tools. Data scienceis an interdisciplinary field that tells us about where the information has come from, what it represents and how business can turn it into a valuable resource.

Now, let’s examine key technologies that you can use to promote your business.

NoSQL databases

Earlier, databases stored structured data in tables with rows and columns. These databases are also called SQL databases and relational databases. Nevertheless, a need for databases that can store data in any format arose. NoSQL databases are database technologies that store unstructured data.

• Document: As implied by its name, it stores information in a document. The exact definition of a document depends on the database.
• Graph: Databases that store information in nodes are called Graph databases, which can further be connected to other nodes. Graph databases make connecting databases and highly complex queries easier and faster.
• Columnar database: This database is most similar to relational databases. This database is not row-oriented to store data, but stores data in a column-oriented manner. As compared to traditional databases, it performs operations faster.
• Key document Value database: The simplified version of the more robust document database is key document value database. Here, each entry is a simple key-value pair. It is simple and easy to implement.

Knowledge discovery tools

Businesses can mine big data—structured and unstructured—stored in multiple sources with the help of knowledge discovery tools. The sources can be different file systems, DBMS (database management systems), APIs (application programming interfaces) or similar platforms. Search and knowledge discovery tools allow businesses to isolate and use the information to their benefit.

In-memory data fabric

In-Memory data fabrics explain the natural evolution of in-memory computing. Data fabrics have a broad approach to in-memory computing, integrating the whole set of in-memory computing use cases into a collection of clear, independent components. A data grid is one of the components that data fabrics provide. In addition to the data grid functionality, the in-memory data fabric includes a CEP (complex event processing) streaming, an in-memory file system, a compute grid and more.

One of the important benefits of an in-memory data fabric is that all of the in-memory components can be used independently while being integrated with each other.

In Apache Ignite, for example, a compute grid can load-balance and schedule computations within a cluster, but if used together with a data grid, the compute grid also routes all the computations responsible for data processing to the cluster members responsible for data caching.

The same applies to streaming and CEP – when working with streamed data, all the processing takes place on the cluster members responsible for caching that data also.

The most common features of in-memory data fabrics are:

• Data Grid
• Compute Grid
• Service Grid
• Streaming & CEP
• Distributed File System
• In-Memory Database
• An Apache Incubator project, Apache Ignite, is the only in-memory data fabric that is available in the open source space.

Data Integration

One of the operational challenges for many businesses dealing with big data is to process terabytes or petabytes of data in a useful way for customer deliverables. With data integration tools, companies can streamline data across various big data solutions such as Apache Hive, Amazon EMR, Apache Pig, Hadoop, Apache Spark, MongoDB, Couchbase, and MapReduce.

Spark

Apache Spark is an open-source processing engine. It is built around speed, user ease, and great analytics. Mainly, it is a parallel data processing framework that can even work with Apache Hadoop, leading to its faster development. It also leads to an ease of streaming along with interactive analysis on your data.

Hadoop

Hadoop is an open-source software framework that stores data and runs applications on clusters of commodity hardware. It provides massive space to store any type of data (structured or unstructured) and has enormous processing power with the ability to handle limitless concurrent tasks virtually. Possessing talent that is well equipped with big data Hadoop training gives any organization, a good head start.

You need to understand the terms used in the definition to understand Hadoop.

Open-source software- Open source is any program where the source code is available for ungated use and even users can modify it to suit their requirements. Usually, an open source project is developed as a public collaboration and is available for free. As Hadoop is an open-source platform, everyone has access to it.

Framework-It includes everything from programs to connections required to develop and run software applications.

Massive storage-The Hadoop framework divides big data into entities, which are stored on clusters of commodity hardware.

Processing power- Hadoop processes bulk data concurrently using various low-cost computers to provide faster results

Data Quality

Data is the most important element of big data processing. Data quality software uses parallel processing to conduct cleansing and enrichment of large data sets to ensure reliable and consistent outputs from big data processing.

Big Data improves operational efficiency, and businesses are able to make informed decisions based on the latest information, and it has become the mainstream norm.

There are several ways to make use of big data to improve your business. It is important for professionals and businesses to remember that while big data is one field, its scope is huge! Make the best use of big data and, who knows? Maybe your company could just become the next Google or Facebook.

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