How Blockchain Enhances Mobile App Development Process in 2019

The best part of emerging technologies is they possess the whole gamut of opportunities. IoT, AR, AI, and Blockchain are a few examples of such disruptive technologies. They are capable of revolutionizing the conventional mobile app development process. Here we are going to discuss how blockchain technology is going to impact the app development and business in 2019.

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Tray.io

Tray.io is ushering in the era of the automated organization. We believe that any organization can and should automate. With Tray.io, citizen automators throughout organizations can easily automate complex processes through a powerful, flexible platform, and can connect their entire cloud stack thanks to APIs. Today businesses such as Segment, Udemy, FICO, and New Relic rely on Tray.io to connect and automate data flow between the tools they use every day. With Tray.io’s visual workflow builder our customers create automated workflows to drive their business processes and do more, faster. We're building a cutting-edge product that is powerful and complete while also being beautiful and easy to use.

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Strategic Approaches to DevOps Issue Detection and Tracking

Article | February 23, 2024

Identify strategic approaches and best practices for tracking and issue detection in DevOps to improve collaboration, streamline operations, and accelerate the delivery of high-quality software. Table of Contents 1. Executive Overview of DevOps 2. Fundamental Tenets of DevOps in Contemporary Business 3. Best Practices for High-impact DevOps Implementation 3.1 Agile Integration 3.2 Effective Use of Microservices Architecture 3.3 Enhance Container Orchestration 3.4 Embrace DevSecOps Integration 3.5 Foster Collaboration 3.6 Implement Test Automation 3.7 Incorporate Infrastructure as Code (IaC) 3.8 Adopt CI/CD 3.9 Deploy Chaos Engineering Methodology 3.10 Adopt Serverless Architecture 3.11 Version Control 3.12 Configuration Management 3.13 Application Performance Monitoring 3.14 Apply Lean Principles 3.15 Monitoring and Logging Metrics 4. Final Thoughts 1. Executive Overview of DevOps DevOps revolutionizes how software is delivered by integrating development and operations to enhance efficiency and speed. It is driven by principles like culture, automation, lean, measurement, and sharing. Successful DevOps adoption streamlines engineering departments and top management, leading to faster and simpler project completion. It's about bridging gaps between teams, focusing on continuous improvement, and connecting user feedback to development for agile market response. Adopting modern DevOps practices is critical for staying competitive, adapting swiftly to changes, attracting top talent, and building customer loyalty​​. 2. Fundamental Tenets of DevOps in Contemporary Business DevOps has emerged as a transformative force in modern business, driven by a set of core tenets that break down silos, accelerate delivery, and foster a culture of continuous improvement. The principles listed below emphasize on breaking down barriers, automation as a foundation, continuous improvement at its core, customer focus as the priority, and shared responsibility as a driving force. Automation Continuous Integration and Continuous Deployment (CI/CD) Collaboration and Communication Rapid and Reliable Delivery Monitoring and Feedback Quality and Security Scalability and Performance Optimization Agility and Flexibility Infrastructure as Code (IaC) Lean and Efficient Operations 3. Best Practices for High-impact DevOps Implementation 3.1 Agile Integration One of the most important aspects of Agile is its emphasis on continuous improvement and customer feedback. Integrating DevOps with Agile enhances value-delivery and streamline workflows, focusing on quick, iterative development and adaptive planning. Agile prioritizes incremental and iterative product delivery, allowing teams to maintain the flexibility and agility for responding to and incorporating stakeholder feedback. Agile follows these 4 core values: Prioritize people and interactions over tools and processes. Prioritize a working product over documentation. Prioritize customer collaboration over contract negotiation. Respond to changes over following a plan. 3.2 Effective Use of Microservices Architecture Being DevOps best practices in modern business, microservices architecture helps develop applications as a collection of small, independent services, and improves scalability and flexibility in development. This modularity allows for easier updates and maintenance, thus smoothening a faster delivery process and improved quality. Microservices enhance the dependability and robustness of systems by enabling the isolation and resolution of service failures without impacting the entire system. Container orchestration with microservices offers fine-grained environments for execution andthe ability to combine various application components into a single instance of an operating system. Microservices, containers, and orchestrators are an ideal complement to one another. 3.3 Enhance Container Orchestration Orchestration manages the lifecycle of containers using DevOps tools for issue detection like Kubernetes, providing automated deployment and scaling. It automates container scheduling, deployment, scalability, load balancing, availability, and networking. Docker introduced a paradigm shift towards distributed operating systems, streamlined software deployment processes, and enabled dependable application execution across diverse computing environments. By automating the deployment of multiple containers used to implement an application, this can be achieved more efficiently. The term used to describe this form of automation is orchestration. It is frequently employed to administer multiple containers through orchestration tools. In addition to mediating between applications or services and container runtimes, container orchestration manages resources, schedules and provides services. This ultimately helps handle the complexities of managing large deployments and reducing manual overhead. It automates provisioning, deployment, networking, scaling, and lifecycle management. 3.4 Embrace DevSecOps Integration Security practices in DevSecOps culture includes automated security checks for software security. It is an approach to automation that integrates security as a shared responsibility throughout the lifecycle entails DevSecOps. Password storage in private repositories for automation is on the decline due to the increasing threats with the development of technology. Therefore,implementing secure internal networks to isolate and track problems in CI/CD workflows is commendable for best strategy for implementing DevOps. One way to restrict exposure to hazards and enforce 'the principle of least privilege'is by utilizing VPNs, robust two-factor authentication, and identity and access management systems. 3.5 Foster Collaboration Encouraging open communication and teamwork across departments, fosters a healthy collaboration, and leads to efficient problem-solving and innovation. Cross-functional collaboration is essential for providing feedback loops and continuously improving work. This improves productivity and reliability. The primary objective of collaboration is to enable a sense of shared responsibility among development and operations teams. Embracing failure and continuous feedback can lead to a transparent and visible environment. Constant monitoring, measuring, and analyzing the software delivery facilitates iterative improvement. 3.6 Implement Test Automation One of the DevOps implementation best practices, this methodology combines operations and development operations in a single cycle. All parties involved in the software development process, including operations, quality assurance, and development, must work closely together. Testing enables constant monitoring of applications and infrastructure while providing feedback to improve development and operational activities. DevOps must establish a mature framework for automated testing that facilitates the programming of test cases. It is advised to start with simple, repetitive tasks and gradually expand coverage to build automation flows efficiently. Each test case should be limited in complexity for easy troubleshooting and built as independent, reusable components to minimize creation time and enhance efficiency. Maintaining separate, self-contained automated test cases also facilitates parallel execution across different environments. 3.7 Incorporate Infrastructure as Code (IaC) IaC helpsto recreate an exact environment subsequent to deployment due to the necessity of updating the systems it interacts with. It manages and provisions infrastructure through code, bringing speed and consistency to the whole environment setup.By eliminating the needfor manual infrastructure management, IaC mitigates the possibility of human error. Instead of relying on engineers to recall previous configurations or respond to failures, the entire system is composed of code and governed by thesource control system.Infrastructure as Code has further decreased cloud expenditures through the implementation of auto-scaling capabilities. 3.8 Adopt CI/CD Continuous Integration is a development methodology that enables programmers to commit changes to a shared repository multiple times daily. Automated builds are utilized to validate the code. This helps the team in the early stages of development in DevOps issue detection and promptly resolving them. It streamlines the software development process by enabling more frequent and reliable updates through regular integration and testing of code. This leads to the early detection of issues and ensures high-quality outputs. Moreover, CI/CD promotes better collaboration among developers and accelerates the feedback loop from users. This is crucial for rapid adaptation to market needs. The automation of deployment processes further reduces errors and saves time, enhancing overall efficiency in software development. 3.9 Deploy Chaos Engineering Methodology Chaos engineering is an approach that aims to enhance the dependability of a system by deliberately introducing failures and atypical situations. Chaos engineers intentionally break the system under controlled conditions to gain a deeper understanding of its vulnerabilities and rectify them before the occurrence of significant problems. Chaos engineering integration into DevOps pipelines enables fault-tolerance and resilience testing to be executed. This helps to identify and address potential system vulnerabilities during the early phases of development. This reduces the time and resources required to resolve and detect DevOps issues after the product has been released. Incorporating chaos experiments into the CI/CD pipeline promotes a culture of ongoing development and knowledge acquisition by enabling teams to promptly observe the consequences of code modifications on the system's overall stability. 3.10 Adopt Serverless Architecture The serverless model allows users to build and run applications and services without managing servers, resulting in one of the DevOps deployment best practices. All infrastructure management tasks are eliminated by this method. These tasks include cluster provisioning, patching, operating system maintenance, and capacity provisioning. Developers are only responsible for bundling their code into containers for deployment. Adopting serverless computing enables developers to delegate the critical tasks of provisioning servers and managing resources to a cloud provider, focusing instead on deploying their code. This strategic approach to DevOps lets the provider automatically manage scalability and resource allocation. Also, it lets them adapt to varying demands efficiently. Function-as-a-Service (FaaS) concept is central to serverless computing, which plays a pivotal role in its functionality. With serverless architectures, developers enjoy enhanced flexibility and accelerated time to release. 3.11 Version Control It is a mechanism that monitors the advancement of code throughout the software development lifecycle and its numerous iterations. Version control assists in change management by maintaining a record of each modification, including authorship, timestamp, and additional pertinent information. Investing in software version enables DevOps teams to improve collaboration among teams. With this, it enables enhanced efficiency in their work. By enabling teams to monitor modifications made to the code base, version control systems facilitate effective collaboration and expedite error resolution. In addition, version management paves the way for deployment of new features and upgrades. Additionally, it guarantees system stability and diminishes the probability of errors that may result in system outages. These systems facilitate the automation of processes by development teams, thereby increasing the speed and efficiency of repetitive activities such as testing, constructing, and deploying. 3.12 Configuration Management Multiple environments are necessary for each phase of the development process. These include unit testing, integration testing, acceptance testing, traffic testing, system testing, and end-user testing. The complexity of these environments escalates as the DevOps testing strategy progresses toward pre-production and production settings. Automated configuration management guarantees that these environments are configured optimally. Inadequate configuration management in DevOps may lead to system outages, data leaks, and breaches. It's also worth noting that bad environments make for improper, incomplete, and shallow tests. 3.13 Application Performance Monitoring Monitoring application performance within the DevOps framework is critical, as it enables the detection and resolution of problems before they can affect the overall system's performance. Although the objective is comparable to that of network performance monitoring (NPM), there are significant distinctions between the two methodologies that render them equally valuable to implement. System and application performance metrics are crucial as they reveal the performance details of the application. For instance, they indicate if the system is too sluggish or if the TPS (transactions per second) SLA is being met. Additionally, they help determine if the system can handle the peak load in the live environment and how the application recovers from a stressed state to a normal state, among other things. 3.14 Apply Lean Principles Applying lean principles in DevOps is recognized as a best practice. The approach enhances efficiency and productivity in software development and delivery processes. It lays emphasis on value creation, waste reduction, and continuous improvement. It aligns seamlessly with DevOps objectives. This involves a thorough understanding of customer needs to define value accurately and then mapping the value stream to identify and eliminate non-value-adding activities. Strategizing a flow in the DevOps pipeline ensures a smooth and uninterrupted delivery process, eliminating problems in DevOps pipelines. The 'pull' system in Lean, adapted to DevOps, ensures that development aligns with customer demand. 3.15 Monitoring and Logging Metrics Organizations analyze logs and metrics to determine how application and infrastructure performance affects the end-user experience of a product. By documenting, categorizing, and analyzing data and records produced by infrastructure and applications, organizations can gain insights into the effects of updates or modifications on users. This facilitates the identification of the underlying factors contributing to issues or unforeseen changes. Active DevOps monitoring strategies become more critical as the frequency of application and infrastructure updates rises, and services must be accessible around the clock. Implementing real-time analysis or setting up alerts for this data enables organizations to monitor their services more proactively. 4. Final Thoughts Looking ahead, the future of DevOps promises even greater innovation and efficiency gains. We can anticipate the continued evolution of automation tools, machine learning, and artificial intelligence to optimize further and accelerate software delivery pipelines. Additionally, incorporating security practices in DevOps culture will enable DevOps to become increasingly vital in safeguarding digital assets. The importance of DevOps in achieving streamlined operations cannot be overstated. By reducing manual interventions, businesses can deliver high-quality software consistently. Fostering collaboration helps them respond to market changes rapidly and ultimately improve customer satisfaction. Moreover, reduced operational costs, faster time-to-market, and increased revenue potential all contribute to a significant ROI.

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Software, Low-Code App Development, Application Development Platform

From Development to Deployment: End-to-End DevOps Automation

Article | August 4, 2023

The seamless journey of DevOps automation in this article, from development to deployment leads to improved revenue, where efficiency meets excellence from initial development to seamless deployment. Contents 1. The Significance of Streamlining Development to Deployment 2. How Key Strategies for DevOps Success Elevate Revenue Generation? 3. Automating Development 3.1. Continuous Integration (CI) Essentials 3.2. Streamlining Continuous Deployment and Delivery (CD) 3.3. Efficient Version Control 4. Configuration Management 4.1. Role-based Configurations in DevOps 4.2. Dynamic Configuration Management (DCM) 5. Top Providers for DevOps Security and Collaboration 5.1. HackerOne 5.2. Teamwork 5.3. Embrace 5.4. Instabug 5.5. Nulab 5.6. Sonar 5.7. LogicMonitor 5.8. JetBrains 5.9. Perforce Software 5.10. Sentry 6. Exploring Emerging Technologies in DevOps Deployment without DevOps is characterized by a traditional, often siloed approach, typically following a linear and sequential 'waterfall' model. Development and operations teams, in such cases, work independently then lead to limited collaboration and communication. The process involves manual handovers, often with insufficient knowledge transfer, resulting in longer release cycles, making deployments riskier and more error-prone due to the bundling of numerous changes. Additionally, the feedback loop is limited, causing delays in implementing user or operational feedback. This is where streamlined deployment comes into the picture. For Example, Etsy, which grew at a regular pace, following the traditional waterfall approach, achieved 80 releases a day rather than deploying code twice a week, post-DevOps integration. 1. The Significance of Streamlining Development to Deployment The previously occurring Integration Issues can now be avoided by shifting the model to DevOps.For example, aligning and integrating the front and backend error-free is no longer tedious. Each developer has their local setup which can cause the infamous 'it works on my machine' syndrome when the code fails to run in different environments. This, ultimately, streamlines the end-to-end DevOps automation process flow. The traditional workflows, often lacking continuous testing and detection of errors, will eliminate costly delays and rework. Collaborating on code, especially in large teams, became a hassle with no shared tools or platforms. While DevOps management platforms enable teams tocollaborate and work together, DevOps collaboration tools and technologies manage work, improve team communication, and share expertise. The market size for custom software development was valued at $388.98 billion in 2020 and is expected to grow to $650.13 billion by 2025. 2. How Key Strategies for DevOps Success Elevate Revenue Generation? An effective DevOps approach prioritizes customers. Focusing on strong software justifies extended development and release timelines. It also ignores the most important factor: software users. Consumers want a good product that solves their problem rather than the process. A good DevOps approach puts the team in the customer's shoes. While 22% of rework time is minimized with DevOps practices, the deployment frequency of firms with DevOps as compared to those without DevOps is a whopping 200 times. Adit Modi, Cloud Architect and Community Leader at Digital Alpha reported that he saw a whopping 30% increase in developer productivity within the first month in a startup. Continuous Integration A robust approach for automating development operations is CI. CI automates code build and change integration. An end-to-end DevOps automation implementation method emphasizes fast, automated systems. Continuous integration lets developers integrate code more often and create and merge code. Implementing CI saves a lot of time in development. It reduces regression bug-resolving time while promoting code quality. A promising CI pipeline helps one understand the codebase and customer features. For example, as stated in Netflix Technology Blog, Titus is Netflix’s infrastructural foundation for container-based applications. It provides Netflix scale cluster, resource management, and container execution with deep Amazon EC2 integration as well as common Netflix infrastructure enablement. Infrastructure-as-a-code and Automation IaC is a compelling way to automate IT infrastructure management, provisioning, and configuration. Maintaining ideal infrastructure is the fundamental goal of IaC. Infrastructure-as-a-code practices in DevOps are utilized to administer the server, storage, and networking infrastructure of a data center. It is designed to facilitate configuration and administration on a massive scale and automatically handles and manages steady-state deviations. Implementing a DevOps automation guide is among the best DevOps strategies for companies seeking to increase revenue as it streamlines development and operations, leading to faster deployment cycles, improved efficiency, and, ultimately, a quicker time-to-market for new and innovative services that drive sales growth. For example, Kloudspot drastically reduced development and service management time by 50% while deploying its new end-to-end DevOps automation infrastructure via an automated system. Constant Delivery and Deployment Continuous delivery (CD) helps teams build high-quality software quickly. Continuous delivery pipelines provide software on time with minimal manual intervention. CD is an effective DevOps release technique for faster software development, testing, and deployment. In an interview with Clare Liguori, Principal Software Engineer for AWS container services, she emphasized that AWS just doesn't enforce automation onto its processes and hope for the best; its automated deployment practices are carefully built, tuned, and tested based on what helps them balance their deployment safety against deployment speed. Microservice framework Microservices are compact, deployable services designed in the style of intricate applications. They are merely an iteration of the Service-Oriented Architecture (SOA) paradigm. In addition to being independent of technology, they exchange information using a variety of communication protocols. The noteworthy part is that one service does not crash or impact other parts of an application! For example, Uber's infrastructure exemplifies a commendable microservice architecture. Several thousand microservices comprise this component and communicate via remote procedure calls (RPC), as noted in their Domain-Oriented Microservice Architecture blog. 3. Automating Development Automated ecosystem orchestration is becoming increasingly important due to the proliferation of Kubernetes architectures, which have transcended the human capacity for management. Continuous integration and deployment are executed in four phases by the CI/CD pipeline: source, build, test, and deploy. Automation in DevOps is mission-critical to an organization’s ability to keep pace with customers and the rapidly changing market. Reducing toil and minimizing toolchain complexity through automation enables developers to focus on what they do best, delivering innovation that drives value for the business. - Hilliary Lipsig, Principal Site Reliability Engineer, RedHat 3.1. Continuous Integration (CI) Essentials A non-CI environment's communication overhead can result in an intricate and entangled synchronization task, thereby increasing project bureaucratic expenses unnecessarily. The manual coordination encompasses operations, the remainder of the organization, and the development teams. CI facilitates the expansion of engineering teams' headcount and output. By integrating CI,software developers are empowered to independently develop features in parallel. Product and engineering communication can be extraordinarily cumbersome. Continuous integration enables this engineering to estimate delivery times for requests when the risk associated with integrating new changes is uncertain. Thereby, CI helps quicken delayed code releases and minimize failure rates. The essentials for an efficient functional CI are: Automate tests to run for every change to the main repository. Run tests on every branch of the repository rather than just focusing on the main branch. Ensure that every feature that gets developed has automated tests. Get a CI service to run those tests automatically on every push to the main repository. Integrate changes regularly. Establish security early in pipelines before building artifacts or deploying. Scan built container images for vulnerabilities with vulnerability scanning for artifact analysis. Implement linting and static code analysis early in pipeline to avoid weaknesses like accepting raw inputs. Use binary authorization to prevent images that contain vulnerabilities from being deployed to clusters. Create pipelines to enable rapid iteration. Ideally, CI pipelines should run in less than 10 minutes. 3.2. Streamlining Continuous Deployment and Delivery (CD) Use GitOps methodology to review changes before they are deployed through merge or pull requests and help in recovery through snapshots in case of failure. Promote, rather than rebuild container images, to avoid minor differences across code branches. Consider using more advanced deployment and testing patterns according to business needs. These include Recreating a deployment, Rolling update deployment, and blue-green deployment. Use Separate clusters for different environments like development environments, pre-production environments (Staging or QA) and production environments. Keep pre-production environments close to production; while pre-production clusters should ideally be identical to production clusters, they can be scaled-down replicas for cost considerations. Implement a clearly defined rollback strategy to address any substantial issues that may arise throughout the deployment process, enabling users to swiftly revert to the previous version, thereby reducing the likelihood of any adverse effects. 3.3. Efficient Version Control Teams typically face these challenges when adopting CI/CD: Conflicts due to manual testing Downtime risk Inefficient resource utilization Kubernetes has the capability to resolve these issues. In a CI/CD pipeline, it decreases the time and effort necessary to develop and deploy applications. The model's effective resource management increases hardware utilization, automates management procedures, and decreases customer-detrimental disruptions. Cluster management, orchestrate deployment and provisioning, and declarative constructs are some specializations performed by Kubernetes. Efficient version control is demonstrated by Kubernetes through the below mentioned techniques: Kubernetes packages applications and their dependenciesthroughcontainers. Enabling consistent versioning of applications, this practice standardizes environments throughout development, testing, and production. DevOps delivery infrastructure considerations are minimized with Kubernetes, which also enhances application resiliency, automation, and scalability. Incorporating version control directly into the deployment process, Kubernetes enables the automated deployment of new application versions and permits a rollback to previous versions ifissues are raised. Kubernetes provides support for a range of deployment strategies, including blue-green and canary deployments. This support enables the gradual rollout and testing of new versions while ensuring the continuity of service. 4. Configuration Management Unit testing, integration testing, acceptance testing, load testing, system testing, and end-user testing are all critical components of a development infrastructure. As testing advances production environments, the complexity of these environments escalates. The role of automation in DevOps ensures that these environments are configured optimally through configuration management. DevOps configuration management automates routine maintenance tasks and frees up development time for the actual programming. 4.1. Role-based Configurations in DevOps Role-based configuration management in DevOps plays a significant role in managing infrastructure updates and creating organized systems. Roles, for example, bundle and organize automation tasks and configurations in a reusable manner, making it easier to handle complex workflows. These roles promote teamwork and efficiency in development​​. Role-based configurations are a sophisticated strategy for delineating access and operational privileges within the CI/CD pipeline tailored to the specific responsibilities of team members. This paradigm leverages identity and access management protocols to automate the enforcement of security policies, reducing the risk of unauthorized changes that lead to system vulnerabilities or compliance breaches. Organizations can create a more secure, stable, and efficient delivery environment by employing a granular control mechanism. As a result, this facilitates a higher caliber of software releases, optimized resource allocation, and an acceleration in delivering value to customers, which in turn can drive revenue growth and competitive advantage. 4.2. Dynamic Configuration Management (DCM) DCM addresses the entire lifecycle of an application. Top organizations implement dynamic configuration management because they recognize its criticality in enterprise-grade platforms. It addresses the limitations of static configuration management, which may hinder developer and Ops productivity. DCM uses a workload specification to create configurations, reducing the number of configuration files needed and simplifying the deployment process. This approach can significantly reduce configuration file numbers, improving overall business performance​​​​​​​​​​​​. For example, an app with ten services and dependencies is deployed across four environments. Assuming an organization deploys three times a day every 21 working days, a typical cloud native setup would be 300 to 600 configuration files with up to 30,000 versions a month. That is up to 95% fewer files than the static approach. 5. Top Providers for DevOps Security and Collaboration 5.1. HackerOne Being the frontrunner in human-powered security, HackerOne outperforms cybercriminals, employing human ingenuity to identify the most critical security vulnerabilities across any attack surface. Integrating cutting-edge artificial intelligence with innovative human intelligence, HackerOne's Attack Resistance Platform mitigates risk throughout the software development lifecycle. Facilitating the identification of novel and illusive vulnerabilities via bug bounties ensures that organizations meet compliance requirements through penetration testing. HackerOne's elite community of ethical hackers empowers businesses to transform themselves confidently. 5.2. Teamwork Teamwork provides a platform that combines user-friendly project administration with industry-leading client operations, which teams adore. It helps you deliver work on time and budget, eliminate client chaos, and understand profitability all in one platform. It provides a single solution for all client operations challenges. Delivering profitable projects, streamlining client operations, and delighting clients help optimize the recurring revenue from retainers. Teamwork.com integrates all the tools a company uses, hence making the company more accessible to manage all in one place. 5.3. Embrace The organization develops and defines a performance management platform for mobile applications that aid in the detection and resolution of any user-impacting problems that may arise within the applications. The mobile environment presents mobile teams with a singular challenge:they must now consider the infinite variety of user and environmental variables across millions of autonomous devices. Embraceliberates the value by ingesting one hundred percent of the time-based technical and user behavior session data.With the help of Embrace, product and engineeringteamshave access to real-time observability while marketing and BI benefit from automated analysis of transformed and usable datasets. 5.4. Instabug Instabug provides a mobile application performance monitoring (APM) platform, offering advanced services that enhance app performance and user experience. It features efficient bug and crash reporting systems, which help quickly identify and resolve app issues, ensuring stability and user trust. With a robust infrastructure supporting over two billion devices, Instabug ensures scalability and reliability. Its simple SDK integration and full visual reproduction tailored for mobile app development challenges make it an essential tool for developers focused on optimizing app functionality and user satisfaction. 5.5. Nulab Nulab is an expanding software company that provides teams of all sizes with online collaboration tools. It places a premium on forward-thinking, collaboration, and trust as an organization to unite groups through technology securely. Its platform provides project management, version control, and bug tracking. Providing Integrated tools to support collaboration at every stage helps in tracking real-time progress. Nulab provides solutions from UX & DEsign to marketing and Quality assurance to plan and strategize beforehand. 5.6. Sonar One of the DevOps security providers, Sonar's market-leading solution empowers development teams and developers to write clean code and automatically remediate existing code, allowing them to concentrate on their favorite tasks and increase the value they deliver to organizations. Its commercial and open-source solutions are compatible with thirty programming languages. SonarLint is a complimentary IDE extension that preventscoding errors. SonarCloud provides straightforward resolution guidance for any Code Quality or Security issue it detects and integrates with existing cloud-based CI/CD workflows. SonarQube is the preeminent instrument developed teams utilize to audit their codebases' code quality and security in real-time and provide direction during code reviews. 5.7. LogicMonitor LogicMonitor's developments demonstrate the company's dedication to a unified monitoring experience based on layered intelligence and hybrid observability. These innovations aim to proactively enhance IT infrastructure and avert issues through quick-to-deploy, SaaS-based automated tools for infrastructure, applications, and business services, focusing on innovation and reducing remediation. A notable advancement is integrating Airbrake error monitoring with LogicMonitor, enabling streamlined usage of both platforms via a single interface. The combined capabilities of LogicMonitor and Airbrake improve visibility and control in IT environment monitoring and management, marking substantial progress in IT operations optimization. 5.8. JetBrains JetBrains core is coding, with its mission to create the most robust and efficient developer tools available. Its tools accelerate production by automating routine checks and corrections, allowing developers more time to develop, explore, and innovate. Regardless of the size of any team, its products provide a seamless experience throughout the process of code development, work planning, and collaboration. YouTrack, a developer tool of JetBrains, is a project management tool that helps deliver efficient products, use agile boards, assist in planning sprints and releases, maintain a strong knowledge base, work with reports and dashboards, and create workflows that follow business processes. 5.9. Perforce Software Perforce Software is an industry leader in DevOps and development solutions that are scalable and optimized to facilitate intelligent testing, dynamic growth, boundaryless collaboration, and risk management. It collaborates with businesses to reduce risk and accelerate time to market in environments where failure has a high cost. Its global specialists provide companies in various industries like automotive, semiconductor, financial services, game development, virtual production, and amusement. Its primary objective is to facilitate solutions that enable its clients to generate innovations rapidly and on a large scale. Perforce is mainly concerned with scalable DevOps by eliminating obstacles to collaboration and productivity. 5.10. Sentry Sentry's performance monitoring eliminates the need for developers to speculate to identify the source of application performance bottlenecks. Rather than delving into performance metrics, Sentry provides the precise lines of code that impede the performance of applications, allowing us to locate the underlying cause. It helps enable notifications before most businesses' users are impacted by performance issues to identify, designate, and resolve within the workflow. It consolidates runtime errors and mobile application malfunction reporting into a single view and provides a real-time, comprehensive assessment of the application's health. By associating errors with releases, identifiers, and devices, one can efficiently resolve issues, reduce customer attrition, and enhance user retention. 6. Exploring Emerging Technologies in DevOps The future of end-to-end DevOps automation promises even greater innovation and efficiency gains. We can anticipate the continued evolution of automation tools, machine learning, and artificial intelligence to optimize further and accelerate software delivery pipelines. Additionally, by incorporating security as a core DevOps principle, DevSecOps will become increasingly vital in safeguarding digital assets. The importance of DevOps’ latest technologies in achieving streamlined operations cannot be overstated. By reducing manual interventions and fostering collaboration, businesses can consistently deliver high-quality software, respond rapidly to market changes, and ultimately enhance customer satisfaction. Moreover, the impact on ROI is substantial, with reduced operational costs, faster time-to-market, and increased revenue potential.

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Software, Future Tech, Application Development Platform

Empowering Industry 4.0 with Artificial Intelligence

Article | August 7, 2023

The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.

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How Artificial Intelligence Is Transforming Businesses

Article | February 12, 2020

Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives. AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity

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Tray.io

Tray.io is ushering in the era of the automated organization. We believe that any organization can and should automate. With Tray.io, citizen automators throughout organizations can easily automate complex processes through a powerful, flexible platform, and can connect their entire cloud stack thanks to APIs. Today businesses such as Segment, Udemy, FICO, and New Relic rely on Tray.io to connect and automate data flow between the tools they use every day. With Tray.io’s visual workflow builder our customers create automated workflows to drive their business processes and do more, faster. We're building a cutting-edge product that is powerful and complete while also being beautiful and easy to use.

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IBM Expands Relationship with AWS to Bring Generative AI Solutions and Dedicated Expertise to Clients

PR Newswire | October 20, 2023

IBM (NYSE: IBM) today announced an expansion of its relationship with Amazon Web Services (AWS) to help more mutual clients operationalize and derive value from generative artificial intelligence (AI). As part of this, IBM Consulting aims to deepen and expand its generative AI expertise on AWS by training 10,000 consultants by the end of 2024; the two organizations also plan to deliver joint solutions and services upgraded with generative AI capabilities designed to help clients across critical use cases. IBM Consulting and AWS already serve clients across a variety of industries with a range of AI solutions and services. Now, the companies are enhancing those solutions and services with the power of generative AI designed to help clients integrate AI quickly into business and IT operations building on AWS. IBM Consulting and AWS plan to start with these specific solutions: Contact Center Modernization with Amazon Connect – IBM Consulting worked with AWS to create summarization and categorization functions for voice and digital interactions using generative AI, which are designed to allow for transfers between the chatbot and live agent and provide the agent with summarized details that expedite resolution times and improve quality management. Platform Services on AWS – Initially introduced in November 2022, this offering is newly upgraded with generative AI to better manage the entire cloud value chain including IT Ops, automation, and platform engineering. The new generative AI capabilities give clients tools to enhance business serviceability and availability for their applications hosted on AWS through intelligent issue resolution and observability techniques. Clients can expect an improvement of uptime and mean time repair which means they can act quickly and effectively to potential issues that arise. Supply Chain Ensemble on AWS – This planned offering will introduce a virtual assistant that can help accelerate and augment the work of supply chain professionals as they aim to deliver on customer expectations, optimize inventories, reduce costs, streamline logistics, and assess supply chain risks. Additionally, for clients looking to modernize on AWS, IBM Consulting plans to integrate AWS generative AI services into its proprietary IBM Consulting Cloud Accelerator to help accelerate the cloud transformation process. This will help with reverse engineering, code generation and code conversion. Commitment to deepening expertise and expanding AWS on watsonx integration IBM has already built extensive expertise with AWS's generative AI services including Amazon SageMaker and Amazon CodeWhisperer, and is one of first AWS Partners to use Amazon Bedrock, a fully managed service that makes industry-leading foundation models (FMs) available through an API, so clients can choose the model that's best suited for their use case. AI expertise and a deep understanding of AWS capabilities are critical for clients looking to implement generative AI, and IBM is already providing mutual clients with access to professionals from IBM Consulting's Center of Excellence for Generative AI with specialized generative AI expertise. With today's news, IBM Consulting plans to train and skill 10,000 consultants on AWS generative AI services by end of 2024. They will have access to an exclusive, partner-only program that provides training on the top use cases and best practices for client engagement with AWS generative AI services. This will help advance their knowledge, allow them to engage with technical professionals and better serve clients innovating on AWS. Enterprise clients are looking for expert help to build a strategy and develop generative AI use cases that can drive business value and transformation – while mitigating risks, said Manish Goyal, Senior Partner, Global AI & Analytics Leader at IBM Consulting. Paired with IBM's AI heritage and deep expertise in business transformation on AWS, this suite of reengineered solutions with embedded generative AI capabilities can help our mutual clients to scale generative AI applications rapidly and responsibly on their platform of choice. IBM is also responding to client demand for generative AI capabilities on AWS by making watsonx.data, a fit-for-purpose data store built on an open lakehouse architecture, available on AWS as a fully managed software-as-a-service (SaaS) solution– which clients can also access in AWS Marketplace. The company also plans to make watsonx.ai and watsonx.governance available on AWS by 2024. This builds on previous commitments made by the two companies to make it easier for clients to consume IBM data, AI and security software on AWS. "Our customers are increasingly looking for the technical support and AI expertise they need to build and implement a generative AI strategy that drives business value from their entire cloud value chain," said Chris Niederman, Managing Director, Global Systems Integrators at AWS. "We are excited to be working with IBM to include embedded generative AI capabilities that assist our mutual customers scale their applications – and help IBM consultants deepen their expertise on best practices for customer engagement with AWS generative AI services." Generative AI at scale for telecommunications Clients are already benefitting from the longstanding relationship between IBM and AWS. Bouygues Telecom, a leading French communications service provider with a history of industry-leading innovation, engaged IBM Consulting to support the company's evolving cloud strategy to explore, design and implement AI use cases at scale while giving teams flexibility to select cloud and AI providers based on departmental and application needs. Leveraging the IBM Garage approach, the team co-designed a custom data and AI reference architecture covering multiple cloud scenarios that can extend to all AI and data projects across Bouygues Telecom's cloud and on-premises platforms. "As we sought to leverage generative AI to extract insights from our engagements with clients, we were confronted with some unfamiliar issues around storage, memory size and power requirements," said Matthieu Dupuis, Head of AI for Bouygues Telecom. "IBM Consulting and AWS have been invaluable partners in identifying the right model for our needs and overcoming these technological barriers." With the new AI platform on AWS, IBM Consulting enabled Bouygues Telecom to develop proof-of-concept models and scale them into production quickly while helping to minimize costs and risks. The platform also enables their data scientists to work with greater efficiency, purpose, and satisfaction by allowing them to spend more time on complex, high-value AI projects rather than launching standalone solutions. An evolving relationship With over 40 years of combined experience on AI solutions, IBM and AWS have been working together to respond to clients who are looking to leverage AI for cost, efficiency, and growth, whether they are looking for a demonstration of the technology, defining potential use cases or full co-creation of bespoke solutions. Additionally, IBM is an AWS Premier Tier Services Partner with over 22,000 AWS certifications globally and has achieved 17 AWS Service Delivery and 16 AWS Competency designations. Today's news builds on this longstanding relationship and a shared value of the importance of enterprise AI. Getting to enterprise AI at scale requires a human-centric, principled approach, and IBM Consulting helps clients establish guardrails that align with the organization's values and standards, mitigate bias and manage data security, lineage, and provenance. IBM Consulting accelerates business transformation for our clients through hybrid cloud and AI technologies, leveraging our open ecosystem of partners. With deep industry expertise spanning strategy, experience design, technology, and operations, we have become the trusted partner to many of the world's most innovative and valuable companies, helping modernize and secure their most complex systems. Our 160,000 consultants embrace an open way of working and apply our proven co-creation method, IBM Garage, to scale ideas into outcomes. Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

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AI Tech

AMD Enhances AI with Nod.ai Acquisition for Open-Source Solutions

AMD | October 11, 2023

AMD acquires Nod.ai to boost open-source AI solutions. AMD's Senior Vice President expects smoother AI deployment with Nod.AI. Nod.AI's SHARK software speeds up AI model deployment, in line with AMD's innovation focus. Advanced Micro Devices (AMD) has announced its definitive agreement to acquire Nod.ai, a leading open-source AI software expert. This strategic move is set to bolster AMD's open-source software strategy and expedite the deployment of optimized AI solutions on their high-performance platforms, including AMD Instinct data center accelerators, Ryzen AI processors, EPYC processors, Versal SoCs, and Radeon GPUs. The acquisition aligns with AMD's broader AI growth strategy, aiming to provide an open software ecosystem that simplifies AI model deployment for customers through developer tools, libraries, and models. Vamsi Boppana, senior vice president, Artificial Intelligence Group at AMD, reportedly remarked, The acquisition of Nod.ai is expected to significantly enhance our ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware. The addition of the talented Nod.ai team accelerates our ability to advance open-source compiler technology and enable portable, high-performance AI solutions across the AMD product portfolio. Nod.ai’s technologies are already widely deployed in the cloud, at the edge and across a broad range of end-point devices today. [Source – Globe Newswire] Nod.ai, known for delivering optimized AI solutions to top hyperscalers, enterprises, and startups, brings its SHARK software, which automates compiler-based optimization. This software minimizes the need for manual fine-tuning, reducing the time required to deploy high-performance AI models across a wide range of data center, edge, and client platforms utilizing AMD CDNA, XDNA, RDNA, and ‘Zen’ architectures. The acquisition reflects AMD's continuous commitment to innovation in high-performance computing, graphics, and visualization technologies. AMD seeks to provide adaptive products that cater to a broad range of industries and applications. It's important to note that this announcement includes forward-looking statements concerning the acquisition's expected benefits and is subject to certain risks and uncertainties. Investors are advised to review AMD's Securities and Exchange Commission filings for a detailed understanding of these risks and uncertainties. Acquiring open-source AI technology may introduce dependence on community support and expertise, potentially leading to security concerns and limited official assistance. Integrating the new software can also result in compatibility issues and market competition in the fiercely contested AI tech sector. However, the acquisition of Nod.ai enhances AMD's AI capabilities, streamlining the deployment of high-performance AI solutions. Embracing an open software strategy lowers entry barriers, and Nod.ai's automation reduces manual optimization needs, enabling deployment across diverse platforms while aligning with AMD's innovation focus.

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AI Tech

Forethought’s Autoflows: AI-Driven Revolution in Customer Support

Forethought | September 25, 2023

Forethought, a leader in generative AI for customer support, has introduced Autoflows, a groundbreaking autonomous resolution capability for SupportGPT. This innovation marks a significant step towards a more efficient, goal-oriented, AI-centric future in customer support. In the fast-evolving landscape of customer support, AI is positioned to revolutionize the way businesses interact with their clients. However, many large enterprises, despite claiming to be AI-first, still rely on outdated manual workflows rooted in the CRM era. These workflows consume hours of valuable time and often lead to subpar performance, dissatisfied customers, and delayed value realization. Forethought aims to transform this outdated approach into a system of intelligence, redefining what it means to be truly AI-first. Deon Nicholas, CEO and Co-Founder of Forethought, reportedly emphasized, Automation over the past decade has focused heavily on rules and tasks and building manual workflows. But the manual workflow is the Achilles heel of the AI-first future. [Source – Businesswire] Autoflows empower customer support leaders to define desired issue resolutions in plain, natural language, eliminating the need for complex decision trees or predefined rulesets. Leveraging AI, Autoflows accurately predicts user needs and determines efficient steps to achieve these desired outcomes. This efficiency allows support agents to redirect their focus towards more complex issues, transcending simplistic task-oriented processes. Autoflows promise significant benefits: Enhanced Customer Experience: Autoflows facilitate natural conversations with customers, employing generative AI models trained on real agent responses and CRM data. Improved Performance: These flows autonomously resolve customer issues and take actions in alignment with agent guidelines, brand preferences, and user prompts. Reduced Time to Value: Autoflows can be created in minutes using natural language, a stark contrast to the days it typically takes to construct intricate decision trees. Brent Pliskow, GM & VP of Customer Support at Upwork, expressed his appreciation for the innovation. He mentioned that they are constantly seeking to incorporate new generative AI capabilities into their customer support processes, with the goal of improving the customer experience and enhancing internal efficiency. He noted that, when they replaced specific manual workflows with Autoflows, they observed significant time savings compared to the traditional approach of building workflows. Additionally, he reported an increase of up to 27% in customer satisfaction in these particular use cases. About Forethought Founded in 2018, Forethought is a prominent generative AI company specializing in customer service automation. Its solutions seamlessly integrate generative AI powered by large language models (LLMs) to enhance support team efficiency. It offers instant case resolution, predictive case prioritization, and agent assistance, all within a single platform. With $90 million in venture capital funding and accolades like G2's Best Software Products for 2023, Forethought is a leader in the tech industry, headquartered in San Francisco, California.

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AI Tech

IBM Expands Relationship with AWS to Bring Generative AI Solutions and Dedicated Expertise to Clients

PR Newswire | October 20, 2023

IBM (NYSE: IBM) today announced an expansion of its relationship with Amazon Web Services (AWS) to help more mutual clients operationalize and derive value from generative artificial intelligence (AI). As part of this, IBM Consulting aims to deepen and expand its generative AI expertise on AWS by training 10,000 consultants by the end of 2024; the two organizations also plan to deliver joint solutions and services upgraded with generative AI capabilities designed to help clients across critical use cases. IBM Consulting and AWS already serve clients across a variety of industries with a range of AI solutions and services. Now, the companies are enhancing those solutions and services with the power of generative AI designed to help clients integrate AI quickly into business and IT operations building on AWS. IBM Consulting and AWS plan to start with these specific solutions: Contact Center Modernization with Amazon Connect – IBM Consulting worked with AWS to create summarization and categorization functions for voice and digital interactions using generative AI, which are designed to allow for transfers between the chatbot and live agent and provide the agent with summarized details that expedite resolution times and improve quality management. Platform Services on AWS – Initially introduced in November 2022, this offering is newly upgraded with generative AI to better manage the entire cloud value chain including IT Ops, automation, and platform engineering. The new generative AI capabilities give clients tools to enhance business serviceability and availability for their applications hosted on AWS through intelligent issue resolution and observability techniques. Clients can expect an improvement of uptime and mean time repair which means they can act quickly and effectively to potential issues that arise. Supply Chain Ensemble on AWS – This planned offering will introduce a virtual assistant that can help accelerate and augment the work of supply chain professionals as they aim to deliver on customer expectations, optimize inventories, reduce costs, streamline logistics, and assess supply chain risks. Additionally, for clients looking to modernize on AWS, IBM Consulting plans to integrate AWS generative AI services into its proprietary IBM Consulting Cloud Accelerator to help accelerate the cloud transformation process. This will help with reverse engineering, code generation and code conversion. Commitment to deepening expertise and expanding AWS on watsonx integration IBM has already built extensive expertise with AWS's generative AI services including Amazon SageMaker and Amazon CodeWhisperer, and is one of first AWS Partners to use Amazon Bedrock, a fully managed service that makes industry-leading foundation models (FMs) available through an API, so clients can choose the model that's best suited for their use case. AI expertise and a deep understanding of AWS capabilities are critical for clients looking to implement generative AI, and IBM is already providing mutual clients with access to professionals from IBM Consulting's Center of Excellence for Generative AI with specialized generative AI expertise. With today's news, IBM Consulting plans to train and skill 10,000 consultants on AWS generative AI services by end of 2024. They will have access to an exclusive, partner-only program that provides training on the top use cases and best practices for client engagement with AWS generative AI services. This will help advance their knowledge, allow them to engage with technical professionals and better serve clients innovating on AWS. Enterprise clients are looking for expert help to build a strategy and develop generative AI use cases that can drive business value and transformation – while mitigating risks, said Manish Goyal, Senior Partner, Global AI & Analytics Leader at IBM Consulting. Paired with IBM's AI heritage and deep expertise in business transformation on AWS, this suite of reengineered solutions with embedded generative AI capabilities can help our mutual clients to scale generative AI applications rapidly and responsibly on their platform of choice. IBM is also responding to client demand for generative AI capabilities on AWS by making watsonx.data, a fit-for-purpose data store built on an open lakehouse architecture, available on AWS as a fully managed software-as-a-service (SaaS) solution– which clients can also access in AWS Marketplace. The company also plans to make watsonx.ai and watsonx.governance available on AWS by 2024. This builds on previous commitments made by the two companies to make it easier for clients to consume IBM data, AI and security software on AWS. "Our customers are increasingly looking for the technical support and AI expertise they need to build and implement a generative AI strategy that drives business value from their entire cloud value chain," said Chris Niederman, Managing Director, Global Systems Integrators at AWS. "We are excited to be working with IBM to include embedded generative AI capabilities that assist our mutual customers scale their applications – and help IBM consultants deepen their expertise on best practices for customer engagement with AWS generative AI services." Generative AI at scale for telecommunications Clients are already benefitting from the longstanding relationship between IBM and AWS. Bouygues Telecom, a leading French communications service provider with a history of industry-leading innovation, engaged IBM Consulting to support the company's evolving cloud strategy to explore, design and implement AI use cases at scale while giving teams flexibility to select cloud and AI providers based on departmental and application needs. Leveraging the IBM Garage approach, the team co-designed a custom data and AI reference architecture covering multiple cloud scenarios that can extend to all AI and data projects across Bouygues Telecom's cloud and on-premises platforms. "As we sought to leverage generative AI to extract insights from our engagements with clients, we were confronted with some unfamiliar issues around storage, memory size and power requirements," said Matthieu Dupuis, Head of AI for Bouygues Telecom. "IBM Consulting and AWS have been invaluable partners in identifying the right model for our needs and overcoming these technological barriers." With the new AI platform on AWS, IBM Consulting enabled Bouygues Telecom to develop proof-of-concept models and scale them into production quickly while helping to minimize costs and risks. The platform also enables their data scientists to work with greater efficiency, purpose, and satisfaction by allowing them to spend more time on complex, high-value AI projects rather than launching standalone solutions. An evolving relationship With over 40 years of combined experience on AI solutions, IBM and AWS have been working together to respond to clients who are looking to leverage AI for cost, efficiency, and growth, whether they are looking for a demonstration of the technology, defining potential use cases or full co-creation of bespoke solutions. Additionally, IBM is an AWS Premier Tier Services Partner with over 22,000 AWS certifications globally and has achieved 17 AWS Service Delivery and 16 AWS Competency designations. Today's news builds on this longstanding relationship and a shared value of the importance of enterprise AI. Getting to enterprise AI at scale requires a human-centric, principled approach, and IBM Consulting helps clients establish guardrails that align with the organization's values and standards, mitigate bias and manage data security, lineage, and provenance. IBM Consulting accelerates business transformation for our clients through hybrid cloud and AI technologies, leveraging our open ecosystem of partners. With deep industry expertise spanning strategy, experience design, technology, and operations, we have become the trusted partner to many of the world's most innovative and valuable companies, helping modernize and secure their most complex systems. Our 160,000 consultants embrace an open way of working and apply our proven co-creation method, IBM Garage, to scale ideas into outcomes. Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

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AI Tech

AMD Enhances AI with Nod.ai Acquisition for Open-Source Solutions

AMD | October 11, 2023

AMD acquires Nod.ai to boost open-source AI solutions. AMD's Senior Vice President expects smoother AI deployment with Nod.AI. Nod.AI's SHARK software speeds up AI model deployment, in line with AMD's innovation focus. Advanced Micro Devices (AMD) has announced its definitive agreement to acquire Nod.ai, a leading open-source AI software expert. This strategic move is set to bolster AMD's open-source software strategy and expedite the deployment of optimized AI solutions on their high-performance platforms, including AMD Instinct data center accelerators, Ryzen AI processors, EPYC processors, Versal SoCs, and Radeon GPUs. The acquisition aligns with AMD's broader AI growth strategy, aiming to provide an open software ecosystem that simplifies AI model deployment for customers through developer tools, libraries, and models. Vamsi Boppana, senior vice president, Artificial Intelligence Group at AMD, reportedly remarked, The acquisition of Nod.ai is expected to significantly enhance our ability to provide AI customers with open software that allows them to easily deploy highly performant AI models tuned for AMD hardware. The addition of the talented Nod.ai team accelerates our ability to advance open-source compiler technology and enable portable, high-performance AI solutions across the AMD product portfolio. Nod.ai’s technologies are already widely deployed in the cloud, at the edge and across a broad range of end-point devices today. [Source – Globe Newswire] Nod.ai, known for delivering optimized AI solutions to top hyperscalers, enterprises, and startups, brings its SHARK software, which automates compiler-based optimization. This software minimizes the need for manual fine-tuning, reducing the time required to deploy high-performance AI models across a wide range of data center, edge, and client platforms utilizing AMD CDNA, XDNA, RDNA, and ‘Zen’ architectures. The acquisition reflects AMD's continuous commitment to innovation in high-performance computing, graphics, and visualization technologies. AMD seeks to provide adaptive products that cater to a broad range of industries and applications. It's important to note that this announcement includes forward-looking statements concerning the acquisition's expected benefits and is subject to certain risks and uncertainties. Investors are advised to review AMD's Securities and Exchange Commission filings for a detailed understanding of these risks and uncertainties. Acquiring open-source AI technology may introduce dependence on community support and expertise, potentially leading to security concerns and limited official assistance. Integrating the new software can also result in compatibility issues and market competition in the fiercely contested AI tech sector. However, the acquisition of Nod.ai enhances AMD's AI capabilities, streamlining the deployment of high-performance AI solutions. Embracing an open software strategy lowers entry barriers, and Nod.ai's automation reduces manual optimization needs, enabling deployment across diverse platforms while aligning with AMD's innovation focus.

Read More

AI Tech

Forethought’s Autoflows: AI-Driven Revolution in Customer Support

Forethought | September 25, 2023

Forethought, a leader in generative AI for customer support, has introduced Autoflows, a groundbreaking autonomous resolution capability for SupportGPT. This innovation marks a significant step towards a more efficient, goal-oriented, AI-centric future in customer support. In the fast-evolving landscape of customer support, AI is positioned to revolutionize the way businesses interact with their clients. However, many large enterprises, despite claiming to be AI-first, still rely on outdated manual workflows rooted in the CRM era. These workflows consume hours of valuable time and often lead to subpar performance, dissatisfied customers, and delayed value realization. Forethought aims to transform this outdated approach into a system of intelligence, redefining what it means to be truly AI-first. Deon Nicholas, CEO and Co-Founder of Forethought, reportedly emphasized, Automation over the past decade has focused heavily on rules and tasks and building manual workflows. But the manual workflow is the Achilles heel of the AI-first future. [Source – Businesswire] Autoflows empower customer support leaders to define desired issue resolutions in plain, natural language, eliminating the need for complex decision trees or predefined rulesets. Leveraging AI, Autoflows accurately predicts user needs and determines efficient steps to achieve these desired outcomes. This efficiency allows support agents to redirect their focus towards more complex issues, transcending simplistic task-oriented processes. Autoflows promise significant benefits: Enhanced Customer Experience: Autoflows facilitate natural conversations with customers, employing generative AI models trained on real agent responses and CRM data. Improved Performance: These flows autonomously resolve customer issues and take actions in alignment with agent guidelines, brand preferences, and user prompts. Reduced Time to Value: Autoflows can be created in minutes using natural language, a stark contrast to the days it typically takes to construct intricate decision trees. Brent Pliskow, GM & VP of Customer Support at Upwork, expressed his appreciation for the innovation. He mentioned that they are constantly seeking to incorporate new generative AI capabilities into their customer support processes, with the goal of improving the customer experience and enhancing internal efficiency. He noted that, when they replaced specific manual workflows with Autoflows, they observed significant time savings compared to the traditional approach of building workflows. Additionally, he reported an increase of up to 27% in customer satisfaction in these particular use cases. About Forethought Founded in 2018, Forethought is a prominent generative AI company specializing in customer service automation. Its solutions seamlessly integrate generative AI powered by large language models (LLMs) to enhance support team efficiency. It offers instant case resolution, predictive case prioritization, and agent assistance, all within a single platform. With $90 million in venture capital funding and accolades like G2's Best Software Products for 2023, Forethought is a leader in the tech industry, headquartered in San Francisco, California.

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