Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other AI technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise — especially with the advances in distributed architectures such as containers, microservices and multi-cloud environments — produce immense volumes of log and performance data that can impede an IT team’s ability to identify and resolve incidents. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.
In today’s rapidly evolving business landscape, companies often find themselves facing challenges related to excess, underutilized, or obsolete assets. These challenges not only tie up valuable resources but also hinder growth and financial efficiency. Fortunately, there’s a silver lining: Asset Recovery Solutions.
What is Asset Recovery?
Asset recovery is a strategic process that involves identifying, valuing, and effectively managing surplus, obsolete, or underutilized assets within an organization. These assets can range from IT equipment, machinery, office furniture, and vehicles, to intellectual property and real estate. When managed correctly, asset recovery can transform an organization’s financial health, streamline operations, and pave the way for growth.
Kay Impex Asset Recovery:
Redefining Asset Recovery Solutions
At the forefront of the asset recovery landscape is Kay Impex Asset Recovery, a trailblazing company renowned for its innovative and client-centric approach. With a proven track record, Kay Impex specializes in delivering comprehensive asset recovery solutions that empower businesses to maximize the value of their surplus assets while minimizing environmental impact.
Key Benefits of Kay Impex Asset Recovery’s Solutions
1. Value Maximization:
Kay Impex Asset Recovery employs a meticulous approach to asset valuation, leveraging a deep understanding of market trends and demand. This ensures that businesses extract the highest possible value from their surplus assets, contributing to improved financial outcomes. By accurately assessing the market value of these assets, Kay Impex enables businesses to make informed decisions that align with their financial goals.
2. Sustainability Focus:
In today’s eco-conscious world, responsible asset disposal is of paramount importance. Kay Impex Asset Recovery prioritizes sustainability by promoting the reuse, refurbishment, and recycling of assets whenever possible, thereby reducing environmental impact and aligning with corporate social responsibility goals. Through its sustainable practices, Kay Impex not only helps organizations achieve their environmental objectives but also enhances their reputation as socially responsible entities.
3. Customized Strategies:
Recognizing that every organization’s asset recovery needs are unique, Kay Impex tailors its solutions to match specific business requirements. This personalized approach ensures that businesses can recover maximum value while aligning with their operational goals. Whether it’s devising a plan for equipment disposition or optimizing the management of surplus inventory, Kay Impex’s customized strategies offer a roadmap to success tailored to each client’s situation.
4. Efficiency and Expertise:
With years of experience, Kay Impex Asset Recovery boasts a team of experts who excel in asset valuation, logistics, compliance, and remarketing. This expertise streamlines the recovery process, minimizing disruptions and optimizing outcomes. From the initial assessment to the final disposition, Kay Impex’s efficient approach allows businesses to save time and resources while ensuring seamless execution.
5. Data Security:
In an era of increasing cybersecurity concerns, Kay Impex places a strong emphasis on data security throughout the asset recovery lifecycle. This ensures that sensitive information is handled with the utmost care, safeguarding the interests of businesses and their clients. By adhering to stringent data security protocols, Kay Impex provides peace of mind to clients, knowing that their confidential information remains protected at all times.
In a world where adaptability and financial efficiency are key to success, asset recovery solutions have emerged as a critical tool for businesses looking to unlock hidden value from their surplus assets. Kay Impex Asset Recovery, a prominent player in the asset recovery landscape, stands as a beacon of innovation and expertise, offering customized strategies that maximize value, promote sustainability, and streamline operations.
By leveraging the comprehensive services of Kay Impex Asset Recovery, businesses can navigate the complex terrain of surplus assets with confidence, transforming what was once a challenge into an opportunity for growth and financial prosperity. As the corporate world continues to evolve, asset recovery remains a cornerstone of strategic decision-making, and Kay Impex Asset Recovery is poised to lead the way towards a more efficient and sustainable future.
In a marketplace where effective asset management can make or break a company’s financial success, Kay Impex Asset Recovery offers a solution that not only addresses the challenges of surplus assets but also opens doors to new possibilities. With a commitment to value maximization, sustainability, customization, expertise, and data security, Kay Impex stands ready to partner with businesses, helping them recover, revitalize, and thrive in today’s dynamic business landscape.
Intel® Virtual RAID on CPU (Intel® VROC) is an enterprise RAID solution that unleashes the performance of NVMe SSDs. Intel® VROC is enabled by a feature in Intel® Xeon® Scalable processors called Intel® Volume Management Device (Intel® VMD), an integrated controller inside the CPU PCIe root complex. Intel® VMD isolates SSD error and event handling from the operating system to help reduce system crashes and reboots. NVMe SSDs are directly connected to the CPU, allowing the full performance potential of fast storage devices, such as Intel® Optane™ SSDs, to be realized. Intel® VROC enables these benefits without the complexity, cost, and power consumption of traditional hardware RAID host bus adapter (HBA) cards placed between the drives and the CPU.
Features of VROC include:
- Enterprise reliability: Increased protection from data loss and corruption in various failure scenarios such as unexpected power loss, even when a volume is degraded.
- Extended Management Tools: Pre-OS and OS management includes HII, CLI, email alerts, and a Windows GUI, all supporting NVMe and SATA controls.
- Integrated caching: Intel® VROC Integrated Caching allows easy addition of a Intel® Optane™ SSD caching layer to accelerate RAID storage arrays.
- Boot RAID: Redundancy for OS images directly off the CPU with pre-OS configuration options for platform set-up.
- High performance storage: Connect NVMe SSDs directly to the CPU for full bandwidth storage connections.
Intel VROC support the below three sets that requires a licensing mechanism to activate:
- Intel VROC Standard
- Intel VROC Premium
- Intel VROC Intel SSD Only
The VROC feature debuted in 2017 to simplify and reduce the cost of high-performance storage arrays, and it has enjoyed broad uptake in enterprise applications. The feature brings NVMe RAID functionality on-die to the CPU for SSD storage devices, thus providing many of the performance, redundancy, bootability, manageability, and serviceability benefits that were previously only accessible with an additional device, like a RAID card or HBA. Thus, VROC gives users a host of high-performance storage features without the added cost, power consumption, heat, and complexity of another component, like a RAID card or HBA, in the chassis — not to mention extra cabling.
In order to have VROC work on a system, the requirements were something like:
- Intel Xeon Scalable (Skylake or newer) system with BIOS support for Intel VMD
- The motherboard needs a header for the VROC hardware key
- A VROC hardware key needs to be installed with the correct level of functionality you want
The term ‘quantum chip,’ or ‘quantum processing unit,’ or ‘QPU’ refers to a physical (fabricated) chip that has several interconnected qubits, or quantum bits. A QPU serves as the basic building block of a complete quantum computer, which also consists of the control electronics, the QPU’s housing environment, and several other parts. A QPU enables a form of computation that’s built on quantum physics— nature’s operating system
How a QPU differs from a CPU
A quantum computer (with QPU) operates in a fundamentally different way from a classical computer. A bit’ in traditional computing (using a CPU) is a very small unit of binary data, which can be either a one or a zero. A processed bit is implemented by one of two levels of low DC voltage in traditional computer systems. The most recent variation of this concept is the ‘qubit,’ or quantum bit. Qubits use the phenomenon of superposition, which enables them to be in numerous states simultaneously, in contrast to binary bits, which can only be in either/or situations.
The capabilities of quantum computers surpass those of traditional computers, especially in terms of speed. A quantum computer can complete a task in two or three stages, whereas a classical computer needs thousands of steps to handle the same problem. Therefore, most quantum chips or QPUs are used as accelerators in heterogeneous multi-core computing devices. These can be connected with a classical processor (CPU) to provide a performance boost that cannot be achieved classically. The classical control circuitry and the quantum processing unit (QPU) are two of the most important components of hardware in quantum computing. Some of the subsystems of the QPU include registers and gates (sometimes called QRAM), a quantum control unit for driving the system states, and circuitry to interface between the classical host CPU and the QPU. However, there are various types of QPUs with variations in the subsystems as well as the underlying principles Quantum computers have the potential to revolutionize the field of computing, but they also come with a number of disadvantages. Some of the main challenges and limitations include noise and DE coherence, scalability, error correction, lack of robust quantum algorithms, high cost, and power consumption. A quantum computer isn’t a supercomputer that can do everything faster. In fact, one of the goals of quantum computing research is to study which problems can be solved by a quantum computer faster than a classical computer and how large the speedup can be. Quantum computers do exceptionally well with problems that require calculating a large number of possible combinations. These types of problems can be found in many areas, such as quantum simulation, cryptography, quantum machine learning, and search problems. As the global community of quantum researchers, scientists, engineers, and business leaders collaborate to advance the quantum ecosystem, we expect to see quantum impact accelerate across every industry.
SaaS, or software-as-a-service, is application software hosted on the cloud and used over an internet connection via a web browser, mobile app or thin client. The SaaS provider is responsible for operating, managing and maintaining the software and the infrastructure on which it runs. The customer simply creates an account, pays a fee, and gets to work.
SaaS applications are sometimes called on-demand software, or hosted software. Whatever the name, SaaS applications run on a SaaS provider’s infrastructure. The provider manages access to the application, including security, availability, and performance.
A good way to understand the SaaS model is by thinking of a bank, which protects the privacy of each customer while providing service that is reliable and secure—on a massive scale. A bank’s customers all use the same financial systems and technology without worrying about anyone accessing their personal information without authorisation.
A multitenant architecture, in which all users and applications share a single, common infrastructure and code base that is centrally maintained. Because SaaS vendor clients are all on the same infrastructure and code base, vendors can innovate more quickly and save the valuable development time previously spent on maintaining numerous versions of outdated code.
The ability for each user to easily customise applications to fit their business processes without affecting the common infrastructure. Because of the way SaaS is architected, these customisations are unique to each company or user and are always preserved through upgrades. That means SaaS providers can make upgrades more often, with less customer risk and much lower adoption cost.
Improved access to data from any networked device while making it easier to manage privileges, monitor data use, and ensure everyone sees the same information at the same time.
SaaS Harnesses the Consumer Web
Anyone familiar wit Office 365 will be familiar with the Web interface of typical SaaS applications. With the SaaS model, you can customise with point-and-click ease, making the weeks or months it takes to update traditional business software seem hopelessly old fashioned.
SaaS takes advantage of cloud computing infrastructure and economies of scale to provide customers a more streamlined approach to adopting, using and paying for software.
Encryption is a way of scrambling data so that only authorized parties can understand the information. In technical terms, it is the process of converting human-readable plaintext to incomprehensible text, also known as ciphertext. In simpler terms, encryption takes readable data and alters it so that it appears random. Encryption requires the use of a cryptographic key: a set of mathematical values that both the sender and the recipient of an encrypted message agree on.
Although encrypted data appears random, encryption proceeds in a logical, predictable way, allowing a party that receives the encrypted data and possesses the right key to decrypt the data, turning it back into plaintext. Truly secure encryption will use keys complex enough that a third party is highly unlikely to decrypt or break the ciphertext by brute force — in other words, by guessing the key.
Data can be encrypted “at rest,” when it is stored, or “in transit,” while it is being transmitted somewhere else.
What is a key in cryptography?
A cryptographic key is a string of characters used within an encryption algorithm for altering data so that it appears random. Like a physical key, it locks (encrypts) data so that only someone with the right key can unlock (decrypt) it.
What are the different types of encryption?
The two main kinds of encryption are symmetric encryption and asymmetric encryption. Asymmetric encryption is also known as public key encryption.
In symmetric encryption, there is only one key, and all communicating parties use the same (secret) key for both encryption and decryption. In asymmetric, or public key, encryption, there are two keys: one key is used for encryption, and a different key is used for decryption. The decryption key is kept private (hence the “private key” name), while the encryption key is shared publicly, for anyone to use (hence the “public key” name). Asymmetric encryption is a foundational technology for TLS (often called SSL).
Why is data encryption necessary?
Privacy: Encryption ensures that no one can read communications or data at rest except the intended recipient or the rightful data owner. This prevents attackers, ad networks, Internet service providers, and in some cases governments from intercepting and reading sensitive data, protecting user privacy.
Security: Encryption helps prevent data breaches, whether the data is in transit or at rest. If a corporate device is lost or stolen and its hard drive is properly encrypted, the data on that device will still be secure. Similarly, encrypted communications enable the communicating parties to exchange sensitive data without leaking the data.
Data integrity: Encryption also helps prevent malicious behavior such as on-path attacks. When data is transmitted across the Internet, encryption ensures that what the recipient receives has not been viewed or tampered with on the way.
Regulations: For all these reasons, many industry and government regulations require companies that handle user data to keep that data encrypted. Examples of regulatory and compliance standards that require encryption include HIPAA, PCI-DSS, and the GDPR.
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more.
Data generated from conversations, declarations or even tweets are examples of unstructured data. Unstructured data doesn’t fit neatly into the traditional row and column structure of relational databases, and represent the vast majority of data available in the actual world Nowadays it is no longer about trying to interpret a text or speech based on its keywords (the old fashioned mechanical way), but about understanding the meaning behind those words (the cognitive way)
It is a discipline that focuses on the interaction between data science and human language, and is scaling to lots of industries. Today NLP is booming thanks to the huge improvements in the access to data and the increase in computational power, which are allowing practitioners to achieve meaningful results in areas like healthcare, media, finance and human resources, among others.
Use Cases of NLP
In simple terms, NLP represents the automatic handling of natural human language like speech or text, and although the concept itself is fascinating, the real value behind this technology comes from the use cases.
NLP can help you with lots of tasks and the fields of application just seem to increase on a daily basis. Let’s mention some examples:
- NLP enables the recognition and prediction of diseases based on electronic health records and patient’s own speech. This capability is being explored in health conditions that go from cardiovascular diseases to depression and even schizophrenia. For example, Amazon Comprehend Medical is a service that uses NLP to extract disease conditions, medications and treatment outcomes from patient notes, clinical trial reports and other electronic health records.
- Organizations can determine what customers are saying about a service or product by identifying and extracting information in sources like social media. This sentiment analysis can provide a lot of information about customers choices and their decision drivers.
- Companies like Yahoo and Google filter and classify your emails with NLP by analyzing text in emails that flow through their servers and stopping spam before they even enter your inbox.
- Amazon’s Alexa and Apple’s Siri are examples of intelligent voice driven interfaces that use NLP to respond to vocal prompts and do everything like find a particular shop, tell us the weather forecast, suggest the best route to the office or turn on the lights at home.
Hyperconverged infrastructure (HCI) is a combination of servers and storage into a distributed infrastructure platform with intelligent software to create flexible building blocks that replace legacy infrastructure consisting of separate servers, storage networks, and storage arrays.Hyper-converged infrastructure (HCI) is a paradigm shift in data center technologies that aims to:
Data Analytics deals with leveraging data to derive meaningful information. The process of Data Analytics primarily involves collecting and organizing Big Data to extract valuable insights, thereby increasing the overall efficiency of business processes.
Data Analysts work with various tools and frameworks to draw lucrative insights.An analyst will focus on how you collect, process, and organize data in order to create actionable results.A data analyst will also find the most appropriate way to present the data in a clear and understandable way. With Data Analysis, organizations are able to take initiatives to respond quickly to emerging market trends; as a result, increase revenue.
Why Data Analytics is Important?
Implementing Data Analytics in various industries can optimize efficiency and workflow. The financial sector is one of the earliest sectors to adopt Data Analytics in banking and finance. For example, Data Analytics is used in calculating the credit score of a person because it takes many factors into consideration for determining the lending risks.Moreover, it helps to predict the market trends and assess risks.
Data Analytics is not limited to focusing on more profits and ROI. It can also be used in the healthcare industry, crime prevention, etc. It uses statistics and advanced analytical techniques to generate valuable insights from the data and help businesses in making better data-driven decisions. Data analytics looks more at statistics and the kinds of data analysis used to connect diverse data sources and trying to find connections between the results.
Remote access technology refers to any IT toolset used to connect to, access, and control devices, resources, and data stored on a local network from a remote geographic location.
This makes remote access crucial for businesses of all sizes which have not moved to a cloud-first model, or which require access to on-premises machines or resources. Three of the most common remote access technologies – Remote Desktop Services, Remote Access Software, and Virtual Private Networks – are examined in brief.