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Blog Posts (44)

  • NLP Introduction - How AI Understands Our Communication Patterns

    Introduction I know like me, as a child, you probably daydreamed about how amazing life would be like to have a walking and talking robot. I used to imagine ordering my robot to make my bed, clean up my room and most definitely do all of my homework! I would have time to spend on the real things that were of much greater importance. Little did I know back then that it is a complicated task to communicate with a machine. Communication is often a two-way process. It is necessary to not only express our thoughts impeccably but to comprehend others’ words without our biases. It is a bonus if we can predict others’ words! It is clear that communication is critical, and to communicate well is a very desirable skill. According to a popular Holmes Report on “The Cost of Poor Communications”, the cost of inadequate communication is staggering for businesses. It is evident that companies that have leaders who are highly effective communicators have relevant business models, higher profits and at least 50% higher returns to shareholders. They constantly seek to learn how they can serve their customers and in turn grow their profits. Diving into The Complexity of Language So, what makes understanding human language so difficult to understand? It is something to ponder over - we humans are certainly not experts at communicating with each other. How can we communicate with a machine? The challenge is attributed to the dynamism of human language. For example, understanding the query intent is a complex process. If we remove all contextual information, then only the key words remain. This can be quite confusing! How would my robot comprehend my direction “Make my bed!”? Does this mean actually hammering wooden boards to construct a bed structure or does it mean to neatly arrange the bedsheets? This illustrates syntactical complexity in human language. We must keep in mind that all human languages have evolved over thousands of years of speech patterns. Language is essentially a fluid, living entity that develops with the needs and situations of communities. If we think about Shakespearean English and the English we speak today, we can easily notice the drastic contrast. According to Grammarly, in Shakespearean times (late 1500s – early 1600), the words bandit, lonely, critic, dauntless, dwindle, elbow, green-eyed (to describe jealousy) and lackluster were created. It is interesting to observe the turbulence during that time – resulting in the creation of new vocabulary. Additionally, the tone, sentiment and mentality of society was very different – all providing a distinctive filter on communication. The Challenge of Human-Machine Understanding As humans we can use our intuitions and communication experiences to understand even if it something not explicitly stated. In contrast, a machine lacks intuition. However, intuition can be developed with the “experience” of a larger corpus. For example, we have sufficient life experiences to understand what a lion is. A computer cannot comprehend what a lion is as an entity or define its attributes. A computer is exceptional at computing the probability of a lion moving as higher than the probability of a piano moving. Regardless of how many layers of natural language methodologies we implement and the quantity of text, it is impossible to recreate human intuition and experience in a machine. Our best approach in converging on the communication gap between human and machine is to represent words relative to other words within a corpus. Natural language processing (NLP) is an umbrella technology encompassing everything from text parsing to complex statistical methods used in deep learning. The aim is to enable communication between a machine and humans. NLP methodologies process human language. NLP allows computers to communicate and comprehend by reading, editing, summarizing text and even generating text. NLP’s Impact on Machine Learning There are many open-source techniques to aid a machine to understand text. Text embedding techniques, such as Word2vec (from Google) and GloVe (from Stanford), provide a general natural language methodology to cultivate an understanding of words, context, sentiment and intent. Words are represented as a dense vector in a highly dimensional sparse matrix. We build a dense vector so that it is similar to vectors of words that appear in similar contexts. Using these vector representations, it is possible to find how different words in a sentence relate to each other and how they relate collectively. The algorithm goes through each position of a word in the text. The word vectors are iteratively adjusted until this probability is maximized. If a word in a sentence is replaced with a word with a comparable vector representation, we can obtain a similar meaning for the sentence. Word2vec performs wonderfully when we have a large corpus. Word2vec training can be improved by eliminating stopwords from the dataset. Stopwords are high frequency words that add little value to language understanding, and their removal aids in improving model accuracy and training time. About Cedrus: If you’d like a guided approach for implementing NLP on your road towards Enterprise-AI, Cedrus Digital specializes in the AI transformation journey for companies of all sizes. Come work with our experts to set you on your path in not only NLP, but Conversational AI, Vision, Predictive Analytics, and Knowledge Graphs as well. We can help you brainstorm& prioritize use cases, as well as help with planning, management, and delivery of AI projects. Let’s partner together. In future blogposts I will dive deeper into the intricacies of text embedding and explore, with some technical rigor, other aspects of NLP required for successful implementation. Businesses have realized that unstructured and semi-structured data need to be mined using NLP rather than rely on outdated manual or template-based techniques. Natural Language Processing is powerful tool that has an immense impact on the business model and serving the customer. Stay tuned! Swati Sharma, Ph.D. is a Senior AI Solutions Engineer at Cedrus Digital. She teaches and mentors future data scientists and works with clients to create solutions for complex business problems.

  • Steps for a successful migration to Red Hat OpenShift Service on AWS (ROSA)

    Enterprises have an abundance of options when considering their long-term modernization and container strategies. With constant innovation and associated change in this maturing space, it is important to choose a path wisely and to strive for consistency and predictability to maximize what really matters most at the end of the day, business value. As enterprises look to optimize their Kubernetes strategies, Red Hat OpenShift quickly emerges as a leading platform to build upon for a variety of reasons. Aligning containerization benefits with enterprise cloud strategies brings forth another no-brainer in leveraging the market leader, Amazon Web Services (AWS), to drive the ultimate combination of flexibility, scalability, and simplicity, which are historically uncommon attributes when referencing enterprise platform architecture. AWS and Red Hat have been collaborating for years to drive innovative solutions in the rapidly evolving space of enterprise technology. The latest release of Red Hat OpenShift Service on AWS (ROSA) combines the best of both worlds to accelerate application modernization with native streamlined efficiencies that enterprises are seeking in today’s increasingly complex enterprise architecture space. For customers looking to embrace the business benefits that ROSA introduces, it is important to map out a winning strategy to ensure a successful migration. Here are 5 key considerations to consider when planning your ROSA migration. 1) Infrastructure and Platform Analysis · Ensure you have complete visibility of your legacy platform for all applications. Leave no stone unturned. · Identify and plan for all platform gaps and application dependencies. · Map functionalities to OpenShift early in the process. 2) Application Analysis · Catalog all critical applications with key attributes such as internal and external dependencies, underlying language, and framework (with versions). 3) CI/CD Analysis · Validate the accuracy of your current pipeline process from a build, dev, release, prod, and validation perspective. · Adapt pipeline stages to leverage OpenShift standards like Helm and OpenShift Operators. · Plan for future native pipelines including Tekton. 4) Code Automation Analysis · Analyze code for trends to remove and replace or update code as needed. · Consistently feed your tooling to drive measurable improvement with each application. · Slow down to speed up, embrace automation at every possible step. 5) Plan for optimization · Embrace OpenShift’s simplified options for automation and governance as code. · ROSA allows for management though the familiar OpenShift interfaces, with simple integration to a growing list of cutting-edge AWS services. · Identify and plan for the art of the possible once you have reached your destination. Focus on accelerating simple use cases as a first step. Lean on the experts from AWS, Red Hat and their partner ecosystems to help ensure your migration is a success that clearly articulates the associated business value. It is important to include assumed benefits that ROSA also represents with greater ability to focus on innovation and business value by leaving the management and support burden up to the experts at Red Hat and AWS. ROSA introduces a low friction, streamlined platform that will uncover areas of efficiency that are unique to each customer and opens endless possibilities for ongoing focus on modernization and innovation. About Cedrus - Cedrus designs, develops, and implements modern cloud applications that drive digital transformation at global brands. We are a trusted advisor for design thinking, innovation and modernization founded on expertise in cloud security, cloud native application development, cognitive business automation and systems integration.

  • Conversational AI Best Practices - Discover AI Opportunities And Their Impact To Your Organization

    Congratulations! You’ve made the decision to continue building yourself as an AI Enabled Enterprise by pursuing the world of Conversational AI. With customizable dictionaries, trainable audio models, and sentiment analysis to capture a caller’s tone, Chat Bots are becoming more viable supplements to your call centers with each passing year. Now that you’re invested, where do you begin? How do you successfully bring a Conversational AI into production? How do you even know which use cases are best worth pursuing? In my 4-Part Best Practice Series , I'll cover the below 9 steps to get a chatbot into production, broken up into business and development categories. In this article, we'll be covering the first two: Business 1. Discover Conversational AI Opportunities 2. Determine Opportunity Impact and Feasibility 3. Plan your project 4. Change Management Development 1. Pre- Development Essentials 2. Data Preparation 3. Test your new Chat Bot 4. Deploy and Maintain your Chat Bot 5. MLOps and Continuous Improvement Business 1. Discover Conversational AI Opportunities The first step to implementing a Chat Bot in your organization is to see where it can have the biggest impact. To do this, it’s recommended to tie your Chat Bot opportunities to overarching company goals, your teams goals, and any problem areas it can help with. For example, say you manage the operational infrastructure of your company’s call center and they’re focused on insurance, you can use the following criteria: Company Goal · Increase membership by 20% Team Goals · Handle 20% more call volume · Improve Net Promoter Score by 1 point · Reduce call duration Problem Areas · Current CSRs cannot keep up during peak hours, resulting in upwards of 1 hour caller wait time · Ramping up and retention of CSRs make it difficult and expensive to scale with growing demand · Low number of SMEs causes calls to drag on while CSRs wait to receive the answers to satisfy Member callers Using the above, you can think about how Conversational AI can help you meet your goals and resolve trouble areas. Let’s break down one of the above pain points and formulate it into a use case: Problem Statement: Members experiencing 1 hour wait times during peak hours Causes: · As flu season comes up, members are calling in to check their coverage · At end of month, members are calling in to make their payments via phone · Claim Escalations require the CSR to reach out to an internal SME, who are limited and require members to once again go on hold How can a chat-bot help? · An FAQ chat bot can answer questions on coverage, freeing up CSRs from redundant questions · A payment chat bot can process payments directly from users without the need of a CSR · CSRs can internally interact with a chat bot to extract data from knowledge bases, reducing the load on SMEs and replying quicker to members Looking at the above, by framing company goals and pain points, you were able to think up of 3 different Conversational AI use cases. Using this method, you can brainstorm many targeted, relevant opportunities to bring Conversational AI into your business. Now that you have your ideas, how do you decide which to tackle first? That will take us to our next step 2. Determine Opportunity Impact and Feasibility Now that we have a list of use cases, the next step is to vet them out and prioritize them. We do this by asking ourselves two different questions: · How much value and impact can this AI opportunity provide? · How complicated will it be to implement? From the use cases we came up with in Step 1, let’s continue this exercise with the Coverage use case. We’ll start by answering the above questions. Impact We determined in the brainstorming exercise that one of the large goals of the company Is to increase membership by 20% in the next year. That will mean call centers need to be scaled up to handle an upwards potential of 20% more calls. Looking at the problems we’ve listed above, hiring, training, and retaining CSRs has not been a trivial task – and handling 20% more call volume while scaling up the team is going to be a difficult and expensive process. All of the use cases we came up with can help alleviate CSRs or SMEs, and now it’s our job to find out by how much. Say that we are able to pull analytics on the conversation transcripts from these member calls. We find that 15% of all calls coming in are members asking questions related to Coverage. With that information, we can start crunching numbers: Assume the following: · 1000 currently employed CSRs · Avg CSR salary is $35,000 a year · Growth goal: +20% That means: · Current spend: 1,000 x $35,000 annually = $35,000,000 · Future Spend: 35,000,000 x 1.20 = $42,000,000 · Increased cost: $7,000,000 Coverage Bot Potential: · 15% of call volume is related to Coverage · Assume that 33% of callers who interact with the bot will ask to be routed to a CSR immediately, which means · 10% of call volume can be covered by Coverage chat bot Putting these numbers together, we can come up with the below value potential: · Future Spend w/ Coverage Chat Bot = Current Spend x (Growth Target – Coverage Bot) · In other words, $35,000,000 x (1.20 – 0.10) = $38,500,000 This gives us our Cost savings estimate for the Coverage chat bot: · Future Spend - Future Spend w/ Coverage Bot = $42,000,000 - $38,500, 000 = $3,500,000 annual cost savings By implementing the Coverage chat bot – we can save this health insurance company $3,500,000 per year in operational costs related to expansion, and even further as growth exceeds the target of 20%. Feasibility We’ve just determined that implementing the Coverage chat bot can save an estimated $3,500,000 during its first year of implementation. Now the next step is figuring out how complex seeing the project to completion will be. To determine how feasible the Coverage chat bot is, we need to ask ourselves a few questions from two different fronts – the business end and the technical end. Let’s start with the business end: Business Questions: · Does the project have an executive sponsor? · Do we have the correct resources available to deliver this project? Do they have capacity? (text/audio conversation transcribers, Dialogue Designers, Bot Developers, backend developers, Voice Engineering, etc.) · How much change management will this require downstream? Does this impact how CSRs currently do their job? Will they need additional training? · How about risk and compliance? Is there PII/PHI data? What about HIPAA? Technical Questions: · Do we need the bot to support chat or voice? Does it need both? · How many conversation logs do we have logged or recorded? How difficult will they be for transcribers to access? · Has a tool suite been selected or do we need to POC different products? If not, how much will procurement of the tools and infrastructure cost? · How complex will the infrastructure workflow look? How many APIs would we need to access? Is it expected to run on-prem? Cloud only? Hybrid? · Is there a DevSecOps workflow that can be utilized for CI/CD, operationalizing, and monitoring? How about MLOps to track performance of any Speech To Text models over time? There is a lot to consider, but answering the above when vetting a new potential Conversational AI use case can save a lot of pain upfront when you determine the level of effort and understand gaps or risks involved. Putting it all together With a value proposition and feasibility determined, you can properly create a roadmap of Conversational AI use cases to implement over time and prioritize them accordingly. Also, if you are looking for internal sponsorship, having clearly defined value propositions goes a long way in securing executive interest. About Cedrus: If you’d like a guided approach in brainstorming and planning Conversational AI opportunities on your way to Enterprise-wide AI, Cedrus Digital specializes in the AI transformation journey for companies of all sizes. Come work with our experts to set you on your path in not only Conversational AI, but NLP, Vision, Predictive Analytics, and Knowledge Graphs as well. Besides brainstorming, we specialize in the planning, management, and delivery of AI projects. Let’s partner together. Like what you’ve read? Stay tuned to our blog for regular posts from our AI experts including best practices, tool selection, the value that each area of AI can provide, case studies, use cases, and more! Ez Nadar is Head of AI Solutions at Cedrus Digital. He helps customers brainstorm, prioritize, plan and deliver Enterprise-Wide AI solutions

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  • RED HAT | Cedrus Digital

    RED HAT The world leader in Hybrid Cloud and Infrastructure Automation, as well as a champion of open source values, is a key partner for Cedrus clients' success. ​ OUR RED HAT EXPERTISE Cedrus is a Red Hat Apex partner with a history of successful execution. We understand the greater ecosystem and competing platforms, so we can help you migrate to the platforms that best fit your business. We can deploy OpenShift on-prem or , and configure your environment with Ansible. on the cloud on AWS (ROSA) Cedrus' Pivotal Cloud Foundry Expertise Cedrus can answer the key questions about Pivotal Cloud Foundry migration: how long will it take and how much will it cost? As experts in PCF as well as Kubernetes and OpenShift development, we have a low-risk, patterned approach from initial analysis through the actual migration. We can handle applications as simple as lift and shift or something as complex as re-engineering. PCF TO OPENSHIFT MIGRATION OFFERINGS OUR RED HAT Red Hat OpenShift Innovate and ship faster with the leading hybrid cloud, enterprise container platform. Red Hat OpenShift is a container platform for Kubernetes that can automate the provisioning, management and scaling of applications so that you can focus on writing the code for your next big idea. Red Hat Ansible Automation Platform Powerful automation across entire IT teams no matter where you are in your automation journey. Ansible is a radically simple IT automation engine that automates , , , , and many other IT needs. cloud provisioning configuration management application deployment intra-service orchestration AMQ Streams High-performance data streaming capability based on the Apache Kafka open source project. OpenShift Serverless (Knative) OpenShift Serverless helps developers to deploy and run event-driven applications that will start based on an event trigger, scale up resources as needed (or to a pre-configured limit), then scale to zero after resource burst. Knative extends Kubernetes to provide a set of components for deploying, running and managing modern applications using the serverless methodology. READY TO GET STARTED ? Get in touch with us! We're thrilled to answer your questions and help you define a vision. Contact Us

  • CLOUD NATIVE | Cedrus Digital

    NATIVE CLOUD Moving to the Cloud is the cornerstone of Digital Transformation. Building Cloud Native products gives you more flexibility, lowers costs, and is critical for organizations to move at a competitive speed as they grow and scale. We use our knowledge of building Cloud Native products, architectural best practices, and our assets to assist you in your journey to the Cloud. OUR CLOUD PARTNERS OUR PROCESS Our Cloud Native experts have extensive experience in your ongoing, iterative journey to the Cloud. Our process will guide you through the process to Cloud maturity and agile implementation. See Our Offerings OUR WORK With 20+ years of successfully delivering enterprise-grade solutions to some of the world’s largest companies, Cedrus is the perfect partner to tackle the most complex enterprise challenges and guide you through your Cloud journey. Our extensive partnerships and expertise with leading Cloud providers allows us to deliver scalable solutions that span Public, Private, Hybrid, and Multi-Cloud. ​ Your Cloud transition introduces a multitude of new opportunities to improve not only the technology you use, but the way your business operates and solves problems. ​ American Commercial Barge League (ACBL) needed to upgrade from an in-house solution that no longer met their growing logistics and technology needs. Cedrus advised in business and technical capacities to diagnose their most pressing issues. We used our years of experience in similar fields to suggest a custom solution that would help them manage logistics through IoT, and expand and enhance their existing software in future-safe AWS technologies. Now, ACBL has a strong, modern infrastructure with the benefits of improved accuracy, efficiency, and user experience. ​ Take a look at their incredible story below! USE CASES See the incredible benefits of moving to the cloud that we've provided to clients in Healthcare, Shipping, Insurance, Banking, and other industries. Explore More Here OFFERINGS CLOUD Assess Cloud Readiness Assessment CI/CD Readiness Assessment Cloud & Architecture Discovery Workshops Organization Security Assessment Measure and Optimize Infrastructure as Code Quickstart and Implementation Workload and Users Onboarding Automation Implementation Policy as Code Quickstart and Implementation AIOps Assessment and Implementation Cost Optimization Assessment and Implementation Cloud Security Assessment and Remediation Application Security Assessment Performance Assessment Cloud Security Monitoring Assessment and Implementation Multi-Cloud Monitoring Assessment and Implementation Proof and Foundation Design Thinking Workshop Project One MVP Implementation Well-Architected Framework Assessment Landing Zone Implementation CI/CD Pipeline Implementation Backup Migration Data Migration Managed Services Application Maintenance and Management Platform Maintenance and Management Cloud Managed Services Workload and User Onboarding Assessment and Automation Implement Cloud Center of Excellence Platform Automation (IaC) DevSecOps Strategy, Framework, and Toolings Data Migration, Data Lake, and Event-Driven Implementation API Strategy & Lifecycle Implementation Application Migration and Modernization IoT Quickstart and Full Implementation BlockChain Quickstart and Implementation Container Strategy and Implementation Serverless Quickstart and Full Implementation PCF Migration More Resources WANT TO LEARN MORE ? Get in touch with us! We're thrilled to answer your questions and help you define a vision. Contact Us

  • AWS | Cedrus Digital

    AMAZON WEB SERVICES Leveraging our years of AWS experience to guarantee secure, scalable, cost-optimized AWS Cloud Adoption. Expand your business value through Cloud adoption, increase your development agility, implement operational excellence, and leverage cutting-edge technologies such as IoT and AI. ​ OUR AWS EXPERTISE We also deploy . Cedrus is a trusted AWS Advanced Consulting Partner. Our long-term partnership is supported by our significant number of AWS certifications and accreditations. We've been recommended by AWS to deliver secured IoT, digital innovation, and modernization projects. Cedrus was featured in AWS' “This is my Architecture” customer testimonial video series (see below). Red Hat OpenShift on AWS (ROSA) OUR SUCCESSES WITH AWS Cedrus' Work on Home IoT Cedrus developed a smart home solution for a major American energy company using AWS services. A gateway device, connected via a serverless solution to a progressive web app, tracks energy usage and controls smart home devices. The customer can also use voice control through Alexa. AWS Greengrass allows the gateway to run a Node.js application, and offers the ability to perform over-the-air updates on the device. Technologies used included AWS IoT, AWS Greengrass, AWS IoT Analytics, API Gateway, AWS Lambda, and Cognito. The team also utilized APIs exposed by OpenHab, a vendor and technology agnostic open source automation software for the home, with an active community. This work was featured at an AWS workshop, where AWS showcased how partners use their technology to interested clients. Cedrus' Work on Industrial IoT Cedrus made a smart home integration for peoples' home devices for a leading American energy company. Customers signed up through their energy provider, were provided an Alexa to control smart home devices, and volunteered their usage statistics and control over their smart home devices. Cedrus built a dashboard for the company to see and control the devices and usage. This solution, now managed by the company, is mutually beneficial: customers save money and energy, and the company does not have to build additional power plants to support unnecessary power usage. Cedrus Featured in the AWS Blog Cedrus reduced FADEL's API generation time from days to less than 30 minutes using our API Czar tool, which features Amazon API Gateway and AWS Lambda. CLICK HERE TO READ THE ARTICLE Our work on AWS Privatelink with API Gateway and Lambda Functions CLICK HERE TO READ THE ARTICLE Learn more about Migrating your messaging infrastructure to Amazon MQ LEARN MORE OFFERINGS OUR AWS Amazon MQ Migration Migrate your on-premise messaging infrastructure to Amazon MQ. . Learn more here Cloud Native Security Framework An offering that assists customers in composing scrum teams that follow agile, pair programming, and test-driven development best practices to deliver cloud-native products Art of the Possible A workshop that brings people together from different departments to formulate new solutions by identifying key business challenges and recommending technology-based solutions that address these challenges Well-Architected Framework Move your existing workload to AWS, leveraging cloud best practices Cloud Migration Enables AWS customers to modernize and move legacy monolithic applications into the cloud, leveraging microservice principles Internet of Things A suite of consultative services that help customers understand how they can legerage AWS IoT services and build a platform to improve their business in new ways Machine Learning Assists AWS customers in developing AI-enabled solutions such as chatbots and/or smart unstructured data intake using AWS Lex, SageMaker, and/or Alexa skills DevSecOps Transformation Synergize development, IT operations, and security teams Blockchain Assists AWS customers in leveraging a hyperledger and quantum ledger database to implement immutable data stores OUR AWS SPECIALTIES Here are some of the ways we can help you accomplish your goals with AWS technology Cloud Native Development Monolithic to Microservices Apache Kafka DevSecOps Automation Data Cloud Migration Internet of Things Serverless and APIs Containers and Orchestration Cloud Security Operational Excellence Mobile Development Hybrid Cloud Strategy AI & Machine Learning Fullstack Development Application Modernization Managed Services READY TO GET STARTED ? Get in touch with us! We're thrilled to answer your questions and help you define a vision. Contact Us

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