The Basics of Software Product Development

February 20, 2019 | 2786 views

Whether you have an elaborate or a high-level idea for a software product, you may find yourself equally confused at the thought of actually launching the development project. Especially so, if your product is a part of an Internet of Things (IoT) solution, includes big data analysis, or should be integrated with other software systems. In this article, we’ll tell you everything you should consider before embarking on your project: from theoretical particularities of software product development to hands-on organizational tips.

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Rang Technologies Inc

Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Rang Technologies has grown to become a global leader in Analytics, Data Science, Artificial Intelligence, Machine Learning, Salesforce CRM, Cloud, DevOps, Internet of Things - IoT, Cybersecurity, IT Consulting and Staffing, and Corporate Training.

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SOFTWARE

The Revolutionary Power of 5G in Automation and Industry Digitization

Article | July 14, 2022

Fifth-generation (5G) mobile phone networks that can carry data up to 50 times faster than major carriers' current phone networks are now rolling out. But 5G promises to do more than just speed up our phone service and download times. The mobile industry's fifth-generation (5G) networks are being developed and are prepared for deployment. The expansion of IoT and other intelligent automation applications is being significantly fueled by the advancing 5G networks, which are becoming more widely accessible. For advancements in intelligent automation—the Internet of Things (IoT), Artificial Intelligence (AI), driverless cars, virtual reality, blockchain, and future innovations we haven't even considered yet—5 G's lightning-fast connectivity and low-latency are essential. The arrival of 5G represents more than simply a generational shift for the tech sector as a whole. Contributions by 5G Networks For a number of reasons, the manufacturing sector is moving toward digitalization: to increase revenue by better servicing their customers; to increase demand; to outperform the competition; to reduce costs by boosting productivity and efficiency; and to minimize risk by promoting safety and security. The main requirements and obstacles in the digitization industry were recently recognized by a study. Millions of devices with ultra-reliable, robust, immediate connectivity. Gadgets, which are expensive with a long battery life. Asset tracking along the constantly shifting supply chains. Carrying out remote medical operations. Enhancing the purchasing experience with AR/VR. Implementing AI to improve operations across the board or in various departments. The mobile telecommunications requirements of the Internet of Things cannot be met by the current 4G and 4G LTE networks. Compared to current 4G LTE networking technologies, 5G can also offer a solution to the problem and the quickest network data rate with a relatively low cost and greater communication coverage. The 5G network's quick speeds will lead to new technical developments. The upcoming 5G technology will support hundreds of billions of connections, offer transmission speeds of 10 Gbps, and have an extremely low latency of 1 ms. Additionally, it makes rural areas' services more dependable, minimizing service disparities between rural and urban areas. Even though the 5G network is a development of the 4G and 4G LTE networks, it has a whole new network design and features like virtualization that provide more than impressively fast data speeds.

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SOFTWARE

AI's Impact on Improving Customer Experience

Article | July 8, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | August 8, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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FUTURE TECH

Language Models: Emerging Types and Why They Matter

Article | July 7, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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Spotlight

Rang Technologies Inc

Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Rang Technologies has grown to become a global leader in Analytics, Data Science, Artificial Intelligence, Machine Learning, Salesforce CRM, Cloud, DevOps, Internet of Things - IoT, Cybersecurity, IT Consulting and Staffing, and Corporate Training.

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AI TECH,GENERAL AI

Cross-Industry Hardware Specification to Accelerate AI Software Development

Intel | September 17, 2022

Arm, Intel and Nvidia have jointly authored a paper describing an 8-bit floating point (FP8) specification and its two variants E5M2 and E4M3 to provide a common interchangeable format that works for both artificial intelligence (AI) training and inference. This cross-industry specification alignment will allow AI models to operate and perform consistently across hardware platforms, accelerating AI software development. Computational requirements for AI have been growing at an exponential rate. New innovation is required across hardware and software to deliver computational throughput needed to advance AI. One of the promising areas of research to address this growing compute gap is to reduce the numeric precision requirements for deep learning to improve memory and computational efficiencies. Reduced-precision methods exploit the inherent noise-resilient properties of deep neural networks to improve compute efficiency. Intel plans to support this format specification across its AI product roadmap for CPUs, GPUs and other AI accelerators, including Habana Gaudi deep learning accelerators. FP8 minimizes deviations from existing IEEE 754 floating point formats with a good balance between hardware and software to leverage existing implementations, accelerate adoption and improve developer productivity. The guiding principle of this format proposal from Arm, Intel and Nvidia is to leverage conventions, concepts and algorithms built on IEEE standardization. This enables the greatest latitude for future AI innovation while still adhering to current industry conventions.

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SOFTWARE

Cycle.io announces partnership with leading custom Software Development firm, Dev.Pro

Cycle.io | August 05, 2022

Cycle.io, a low-ops application deployment platform, recently announced the Cycle Partner Program, and today we are happy to add Dev.Pro as one of our new Development Agency Partners. Our partnership empowers Dev.Pro development teams to build and deploy advanced solutions, products, and services efficiently on the Cycle Platform. Dev.Pro, a US based software, custom development partner, works with customers across many industries using state of the art technologies. Dev.Pro enables their customers to grow by expediting time to market and delivering new products and features in a cost effective and efficient manner. This new partnership with Cycle expands the Dev.Pro portfolio and ensures that customers continue to have access to the latest solutions. “The Cycle & Dev.Pro partnership is a natural fit for both organizations. Cycle offers a reliable, scalable, and feature rich platform; while Dev.Pro has the engineering expertise and experience to build and support even the most complex environments. ” Brian Agee, Chief Revenue Officer, Dev.Pro. Cycle’s all-in-one platform has been used to deploy over a million containers, becoming the bridge between our partners' infrastructure and their code. The platform offers increased flexibility to scale their solutions utilizing Cycle’s rich set of features via the portal or through their powerful API. “We are extremely excited to have Dev.Pro join our partner program. Dev.Pro has a reputation for having impressive engineering skills and outperforming customer expectations. Working with the Dev.Pro team over the course of the past six months indicates that this is only the tip of the iceberg”. Karl Empey, Head of Sales, Cycle.io. About Cycle Cycle is the all-in-one low-ops platform built for deploying, scaling, and monitoring your websites, backends, and APIs. With automatic platform updates, standardized deployments, a powerful API, and bottleneck crushing automation—the platform offers batteries included, and no DevOps team required. Founded in 2015, the company is headquartered in Reno, NV. About Dev.Pro Dev.Pro is a software development partner that allows innovative technology companies to amplify their growth ambitions and expedite time-to-market.Result-driven. Quality-obsessed. Dev.Pro builds teams that deliver a custom software development experience to meet any skillset, complexity, or scale. With a carefully curated staff of more than 850 highly skilled specialists, Dev.Pro offers a wide range of technical expertise including: Software Development, Cloud, DevOps, UI/UX Design, System Integration, Reporting/Analytics, and Manual/Automation Testing. Dev.Pro is a globally distributed company operating in 50+ countries spread across 5 continents.

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AI TECH

Inspur Information's AIStation Works With NVIDIA AI Enterprise Software Suite to Power Innovation

Inspur Information | August 03, 2022

Inspur Information, a leading IT infrastructure solutions provider, is combining AIStation, its unified management and scheduling AI computing resource platform, with NVIDIA AI Enterprise, a cloud-native suite of AI and data analytics software, to provide enterprise users with a convenient and efficient platform for utilizing AI computing resources. This includes professional development, advanced deployment tools, and state-of-the-art components. The combined platform allows enterprises to quickly implement the industry's most advanced AI capabilities and rapidly deploy AI applications to drive intelligent business innovation. There have been multiple attempts to build AI resource stacks for improving AI innovation and implementation. However, implementing AI technologies is a very challenging task. Enterprises not only need to meet requirements for computing power, but also maximize their computing resource utilization, quickly build and manage AI deployment environments, and efficiently introduce AI capabilities. It requires an AI platform that supports agile development and necessitates easy management and deployment to quickly implement AI applications and accelerate their performance. Inspur Information's AIStation is an end-to-end platform that provides one-stop support for AI development and deployment. With powerful resource scheduling and management capabilities, it accelerates the development and deployment of AI technologies. NVIDIA AI Enterprise is an optimized end-to-end, cloud-native AI and data analytics software suite. It supports the processes from environment construction, data processing, training, and inference to allow for faster development and implementation of AI technologies. AIStation's superior scheduling, management and monitoring capabilities are able to fully unleash NVIDIA AI Enterprise's excellent AI development and deployment capabilities. The combined solution expertly handles data storage, computing power and task scheduling, cluster operation and maintenance, and provides enterprises with an agile and easy-to-use AI platform that can fully utilize their AI computing resources. It also enhances the development and deployment of AI-related tools and components. Resulting AI development and deployment is markedly sped up, making AI transformation more efficient, and improving the business value of AI. Detailed benefits of the combined Inspur Information and NVIDIA AI Enterprise solution include: Maximizing computing resources The solution makes it easier for enterprise customers to use and manage hardware infrastructure including NVIDIA GPUs, and supports Multi-Instance GPU features in the NVIDIA Ampere architecture, enabling users to centrally allocate and monitor computing resources and efficiently allocate those computing resources in large-scale computing power clusters. Acquiring AI capabilities quickly The cloud-native environment management mechanism based on Docker containers gives users an effort-free experience for building cloud-native development environments. Thanks to the powerful scheduling and management capabilities of Inspur Information's AIStation and the professional frameworks, models, applications and development tools of NVIDIA AI Enterprise, users can build AI environments and run AI tasks with ease to achieve fast delivery and quickly introduce advanced AI capabilities. Managing AI infrastructure with ease The multi-tenant management policy allows cluster administrators to focus on managing hardware infrastructure and controlling user quotas. Users can manage AI services and perform AI production within quota limits, and developers can quickly obtain AI compute resources without needing to be audited by administrators often. In addition, administrators can monitor key services and performance of the platform in real-time, and improve system availability by leveraging the fault-tolerant mechanisms and high-availability architecture. Today, AI technologies are changing various industries such as finance, transportation, medical care, education, and scientific research. By introducing AI technologies and applications, enterprises can boost their business capabilities and create greater value. Inspur Information is working closely with NVIDIA to promote the construction of intelligent computing infrastructure and develop AI capability stacks to further accelerate enterprises' AI strategies and AI transformation. Inspur Information and NVIDIA have a longstanding collaboration, with Inspur AI servers supporting the full lineup of NVIDIA GPUs, including the NVIDIA A100, A30, A10, and A2. Inspur AI servers were among the first in the industry to fully support NVIDIA Ampere architecture-based GPUs and the company has obtained NVIDIA-Certified System status for its AI servers. About Inspur Information Inspur Information is a leading provider of data center infrastructure, cloud computing, and AI solutions. It is the world’s 2nd largest server manufacturer. Through engineering and innovation, Inspur Information delivers cutting-edge computing hardware design and extensive product offerings to address important technology sectors such as open computing, cloud data center, AI, and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges.

Read More

AI TECH,GENERAL AI

Cross-Industry Hardware Specification to Accelerate AI Software Development

Intel | September 17, 2022

Arm, Intel and Nvidia have jointly authored a paper describing an 8-bit floating point (FP8) specification and its two variants E5M2 and E4M3 to provide a common interchangeable format that works for both artificial intelligence (AI) training and inference. This cross-industry specification alignment will allow AI models to operate and perform consistently across hardware platforms, accelerating AI software development. Computational requirements for AI have been growing at an exponential rate. New innovation is required across hardware and software to deliver computational throughput needed to advance AI. One of the promising areas of research to address this growing compute gap is to reduce the numeric precision requirements for deep learning to improve memory and computational efficiencies. Reduced-precision methods exploit the inherent noise-resilient properties of deep neural networks to improve compute efficiency. Intel plans to support this format specification across its AI product roadmap for CPUs, GPUs and other AI accelerators, including Habana Gaudi deep learning accelerators. FP8 minimizes deviations from existing IEEE 754 floating point formats with a good balance between hardware and software to leverage existing implementations, accelerate adoption and improve developer productivity. The guiding principle of this format proposal from Arm, Intel and Nvidia is to leverage conventions, concepts and algorithms built on IEEE standardization. This enables the greatest latitude for future AI innovation while still adhering to current industry conventions.

Read More

SOFTWARE

Cycle.io announces partnership with leading custom Software Development firm, Dev.Pro

Cycle.io | August 05, 2022

Cycle.io, a low-ops application deployment platform, recently announced the Cycle Partner Program, and today we are happy to add Dev.Pro as one of our new Development Agency Partners. Our partnership empowers Dev.Pro development teams to build and deploy advanced solutions, products, and services efficiently on the Cycle Platform. Dev.Pro, a US based software, custom development partner, works with customers across many industries using state of the art technologies. Dev.Pro enables their customers to grow by expediting time to market and delivering new products and features in a cost effective and efficient manner. This new partnership with Cycle expands the Dev.Pro portfolio and ensures that customers continue to have access to the latest solutions. “The Cycle & Dev.Pro partnership is a natural fit for both organizations. Cycle offers a reliable, scalable, and feature rich platform; while Dev.Pro has the engineering expertise and experience to build and support even the most complex environments. ” Brian Agee, Chief Revenue Officer, Dev.Pro. Cycle’s all-in-one platform has been used to deploy over a million containers, becoming the bridge between our partners' infrastructure and their code. The platform offers increased flexibility to scale their solutions utilizing Cycle’s rich set of features via the portal or through their powerful API. “We are extremely excited to have Dev.Pro join our partner program. Dev.Pro has a reputation for having impressive engineering skills and outperforming customer expectations. Working with the Dev.Pro team over the course of the past six months indicates that this is only the tip of the iceberg”. Karl Empey, Head of Sales, Cycle.io. About Cycle Cycle is the all-in-one low-ops platform built for deploying, scaling, and monitoring your websites, backends, and APIs. With automatic platform updates, standardized deployments, a powerful API, and bottleneck crushing automation—the platform offers batteries included, and no DevOps team required. Founded in 2015, the company is headquartered in Reno, NV. About Dev.Pro Dev.Pro is a software development partner that allows innovative technology companies to amplify their growth ambitions and expedite time-to-market.Result-driven. Quality-obsessed. Dev.Pro builds teams that deliver a custom software development experience to meet any skillset, complexity, or scale. With a carefully curated staff of more than 850 highly skilled specialists, Dev.Pro offers a wide range of technical expertise including: Software Development, Cloud, DevOps, UI/UX Design, System Integration, Reporting/Analytics, and Manual/Automation Testing. Dev.Pro is a globally distributed company operating in 50+ countries spread across 5 continents.

Read More

AI TECH

Inspur Information's AIStation Works With NVIDIA AI Enterprise Software Suite to Power Innovation

Inspur Information | August 03, 2022

Inspur Information, a leading IT infrastructure solutions provider, is combining AIStation, its unified management and scheduling AI computing resource platform, with NVIDIA AI Enterprise, a cloud-native suite of AI and data analytics software, to provide enterprise users with a convenient and efficient platform for utilizing AI computing resources. This includes professional development, advanced deployment tools, and state-of-the-art components. The combined platform allows enterprises to quickly implement the industry's most advanced AI capabilities and rapidly deploy AI applications to drive intelligent business innovation. There have been multiple attempts to build AI resource stacks for improving AI innovation and implementation. However, implementing AI technologies is a very challenging task. Enterprises not only need to meet requirements for computing power, but also maximize their computing resource utilization, quickly build and manage AI deployment environments, and efficiently introduce AI capabilities. It requires an AI platform that supports agile development and necessitates easy management and deployment to quickly implement AI applications and accelerate their performance. Inspur Information's AIStation is an end-to-end platform that provides one-stop support for AI development and deployment. With powerful resource scheduling and management capabilities, it accelerates the development and deployment of AI technologies. NVIDIA AI Enterprise is an optimized end-to-end, cloud-native AI and data analytics software suite. It supports the processes from environment construction, data processing, training, and inference to allow for faster development and implementation of AI technologies. AIStation's superior scheduling, management and monitoring capabilities are able to fully unleash NVIDIA AI Enterprise's excellent AI development and deployment capabilities. The combined solution expertly handles data storage, computing power and task scheduling, cluster operation and maintenance, and provides enterprises with an agile and easy-to-use AI platform that can fully utilize their AI computing resources. It also enhances the development and deployment of AI-related tools and components. Resulting AI development and deployment is markedly sped up, making AI transformation more efficient, and improving the business value of AI. Detailed benefits of the combined Inspur Information and NVIDIA AI Enterprise solution include: Maximizing computing resources The solution makes it easier for enterprise customers to use and manage hardware infrastructure including NVIDIA GPUs, and supports Multi-Instance GPU features in the NVIDIA Ampere architecture, enabling users to centrally allocate and monitor computing resources and efficiently allocate those computing resources in large-scale computing power clusters. Acquiring AI capabilities quickly The cloud-native environment management mechanism based on Docker containers gives users an effort-free experience for building cloud-native development environments. Thanks to the powerful scheduling and management capabilities of Inspur Information's AIStation and the professional frameworks, models, applications and development tools of NVIDIA AI Enterprise, users can build AI environments and run AI tasks with ease to achieve fast delivery and quickly introduce advanced AI capabilities. Managing AI infrastructure with ease The multi-tenant management policy allows cluster administrators to focus on managing hardware infrastructure and controlling user quotas. Users can manage AI services and perform AI production within quota limits, and developers can quickly obtain AI compute resources without needing to be audited by administrators often. In addition, administrators can monitor key services and performance of the platform in real-time, and improve system availability by leveraging the fault-tolerant mechanisms and high-availability architecture. Today, AI technologies are changing various industries such as finance, transportation, medical care, education, and scientific research. By introducing AI technologies and applications, enterprises can boost their business capabilities and create greater value. Inspur Information is working closely with NVIDIA to promote the construction of intelligent computing infrastructure and develop AI capability stacks to further accelerate enterprises' AI strategies and AI transformation. Inspur Information and NVIDIA have a longstanding collaboration, with Inspur AI servers supporting the full lineup of NVIDIA GPUs, including the NVIDIA A100, A30, A10, and A2. Inspur AI servers were among the first in the industry to fully support NVIDIA Ampere architecture-based GPUs and the company has obtained NVIDIA-Certified System status for its AI servers. About Inspur Information Inspur Information is a leading provider of data center infrastructure, cloud computing, and AI solutions. It is the world’s 2nd largest server manufacturer. Through engineering and innovation, Inspur Information delivers cutting-edge computing hardware design and extensive product offerings to address important technology sectors such as open computing, cloud data center, AI, and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges.

Read More

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