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Blog Posts (47)

  • Our secret to building winning teams

    What's our secret to winning IBM's Build-A-Bot Challenge? It's the same as our secret to providing the solutions and high-level of leadership that clients have come to expect from such a small (but growing) firm: a real, tangible, company-empowered growth mindset. While many companies like to list employee growth and development as a bullet point in a list of values, Cedrus delivers on the promise to a depth that I haven't seen from anyone else. I'm relatively new to Cedrus, but I can already see the results of this mindset manifesting in all aspects of the company and how we stand out to our customers. I was told in my first interview that Cedrus cares less about what specific tools and technologies I know how to work with, and more about my capability to learn new things and contribute as part of a team, since new techniques can easily be learned, but one's ability to grow and be a value-adding collaborator are much harder to cultivate. Then, they back this mindset up with an encouragement to go learn whatever we feel is necessary to succeed and grow. Essentially: Take whatever time you need for learning Just don't bill the client for this time Feel free to expense any learning-related costs But we ask that you run it past management first if the cost will be significant And this goes into effect on day 1: I expensed some courses within my first week. Meanwhile, other companies impose limits, like requiring someone to be employed with the company for a year before any training expenses would be approved or annual spending caps that wouldn't be high enough for a conference or a certification. Further, this mindset extends far beyond structured or conventional training: Want to read some research papers? Please do! Wanna form a team to enter a hackathon? How can we help? The company realizes that self-directed, independent learning is crucial to not only growing someone's skills, but also keeping them engaged in their work, looking for ways to reward and empower people who go out of their way to better themselves since it betters the company at the same time. So when a coworker suggested that we form a team to enter the Build-A-Bot Challenge (with only about 2 weeks remaining), we didn't have to stress out about keeping this side-project secret; we knew that the company would support us in our efforts. Besides the intangible peace of mind, we also were able to do things like: poll our department for feedback on our solution idea, call out work on the project as part of our workload in status update meetings, and even post our submission video on our company YouTube channel. That's all well and good, but how does a project like this help Cedrus' clients? This IBM hackathon facilitated a level of knowledge transfer, experience building, and design thinking that could not be replicated through other means, and all in a short amount of time and at no additional cost. Together our team went through the entire development lifecycle, from architecting a solution, to planning sprints, developing a prototype, iterating the user experience, and deployment. While many of my team members have been through their fair share of full lifecycles, each time through is a learning opportunity, and I was able to learn so much from each of their strengths this time through, leaving me much better equipped to serve our clients and deliver solutions. And this hackathon isn't a rare occurrence; Cedrus also invests in R&D to ensure our consultants are well versed, knowing when cutting-edge research is the right solution for our customers' needs. Cedrus delivers growth for our clients by growing people who like to grow.

  • Audio data with deep learning - the next frontier

    Introduction Artificial intelligence (AI) has become an inherent part of our lives and machine learning is driving to solve new problems every day. With the recent advancements in deep learning, most AI practitioners agree that AI's impact is exponentially expanding year after year. This, of course, has been with the help of big data and unstructured data. These two types of data are not synonymous. Big data can be structured or unstructured and has three characteristics - volume, velocity (speed with which it arrives) and variety (different types like pictures, videos etc.) in data. Unstructured data, on the other hand, refers to data that is not organized or repetitive and is oftentimes large in volume and velocity and thus can be synonymously referred to as big data. Two well-known examples of unstructured data are images and text. Image data is used to solve complex computer vision problems such as facial recognition and autonomous cars. Text data, on the other hand, is used to solve Natural Language Processing (NLP) problems such as understanding spoken language or translating from one language to another (refer to our NLP blog here). Because of its applications, image and text data have received a lot of attention. Along with images and text, there's a third type of unstructured data - audio data. Audio data is less well known, and we'll be diving into it in this writing. This type of data comes in the form of audio files (.wav, .mp3 etc.) or streaming audio. Most applications of audio data are in the music domain in the form of music cataloging or lyric generation. Complexity of audio data has limited it from finding mainstream applications. This has changed with the rapid development in the field of deep learning. Audio data applications Audio data is used to build AI models that perform automatic speech recognition (ASR). ASR solves problems such as understanding what is said to a voice assistant such as Alexa or Siri or converting speech to text for applications such as voice bots and automatic medical transcription. In addition to ASR, audio data is also used to solve problems such as speaker detection, speaker verification and speaker diarization. Applications of speaker detection include Alexa’s ability to change responses based on who is speaking or identifying speakers in a live-streaming audio or video. An application of speaker verification is biometric security. Speaker diarization refers to separating audio to identify who is speaking “what” and “when”. A common application of speaker diarization is transcribing meeting recordings or phone conversations by speaker. As this technology ripens, many more applications are possible that would be based on conversations between people e.g., automatic test result generation for school verbal tests, mental health diagnosis based on conversations. Features of audio data Unlike text and image data, audio data has hidden characteristics in its signal which tend to be more difficult to mine. Most audio data available today is digitized. The digitization process stores audio signals by sampling them. The sampling rate varies by the type of media. For example, CD quality audio uses a sampling rate of 44,100. This means that audio data is sampled 44,100 times in a second and stored in a digital format. Each sample value represents the intensity or amplitude of the sound signal. This sampled data can be processed further to extract features depending on what kind of analysis is required. Spectral features that are based on the frequency domain are probably the most useful. Examples of such features and their applications are as follows (there are many more): 1. Mel Frequency Cepstral Coefficients (MFCC) - represents the envelope of time power spectrum which represent sounds made by a human vocal tract 2. Zero crossing rate – used to detect percussive sounds and a good feature for classifying musical genres 3. Average Energy – can represent formants which uniquely identify a human voice 4. Spectral entropy – used to detect silence A speech model will extract the above features depending on the application and use them in a supervised or unsupervised machine learning model or in a deep learning model. Models for speaker detection, verification and speaker diarization Models for speaker detection and speaker verification are classification problems. For speaker detection, audio features must be extracted for each speaker. The audio feature data can be fed to a neural network which can then be trained. Models for speaker diarization have historically been unsupervised clustering problems but newer models are based on neural networks. Speech model performance Performance of speech models have yet to overcome the following challenges: (1) poor accuracy in recordings of people of the same gender or of people with different accents (2) poor accuracy of speech to text due to language complexities and (3) inability to deal with background noises. The first challenge can be overcome with more training data. New methodologies in bringing together acoustic data and text data is addressing the second challenge. Speech denoising (removal of background noise) is another area that requires a lot of noise and quiet speech samples. Overall, one can expect that speech models will perform better with more varied data. This is the case for deep learning models in other areas as well. As building complex deep learning models becomes easier with various frameworks, a majority of the work is in understanding and preparing the data. Conclusion Audio data is coming into prime time along with its cousins – image and text data. The main driver for it has been deep learning. Applications such as voice assistants and voice bots have entered the mainstream due to this technology development. With a broad spectrum of models in the area of ASR, speaker detection, speaker verification and speaker diarization, we can expect a larger array of conversation-based applications. Crossing this frontier will require integrating multiple types of data and preparing them well so that they are ingested by advanced models to produce good predictions. About Cedrus: Cedrus Digital is involved in studying audio and conversation data and provides strategies on how information can be harvested from them. Cedrus Digital provides analytics and data science services to gain visibility into high volume call center inquiries – creating opportunities for process efficiencies and high value Conversational AI call center solutions and supplementation. Chitra Sharathchandra is a Data Scientist who enjoys working on implementable AI solutions related to multiple types of data

  • An Introduction to the World of Knowledge Graphs

    Introduction Data lakes, data warehouse, RDBMs, NOSQL, SharePoint, and Excel, today’s enterprises have an overabundance of data stored across different organization and technologies. However, the promise of big data’s ability to provide insights and revolutionize analytics has not fully materialized. In this post, we’ll take a look at why that is and how to achieve valuable insights into your data. Most companies today are data driven and there has been an explosion in volume driven by accessible and affordable storage. This is along with digitization of data and an explosion in IOT. Despite all this, many companies struggle with finding a return on investment. What drives this unrealized potential is not the lack of data or the storage/access technology but rather the lack of knowledge. This where Knowledge Graphs play a role in helping enterprises realize value in their data. Knowledge Graphs Empower AI Capabilities But what is a Knowledge Graph? A Knowledge Graph connects data across different sources (structured and unstructured) and provides a semantic enriched structure that enables discovery, insight and empowers AI capabilities. It can also be viewed as a network of objects with semantic and functional relationships between the different connected objects/things. The more relationships created, the more context the data objects/things have, which then provides a bigger picture of the whole situation, helping users make informed decisions with connections that they may have never found. Although a knowledge graph relies on a graph database (technology) to store and process data, it is the data, connectivity and ontology that transforms a graph database with properties to a knowledge graph. For example, an object node that has the name PAM has little meaning to a computer or an algorithm (and most individuals). There is no context to associate PAM with an infection or what relationships that infection may have with propagation mechanisms or preventive measures. A knowledge graph resolves this by labelling the PAM node as an infection; and by associating the node to an infection ontology an algorithm can start to understand the PAM entity in context with other node types (e.g., propagation mechanism, medication, preventive measures) that may also be in the knowledge graph. In summary a knowledge graph understands real-world entities and their relationships to one another.’ The Key Benefits of Knowledge Graphs Combine Disparate Data Silos: Knowledge Graphs help to combine disparate silos of data, giving an overview of all the organization’s knowledge – not only departmentally but also across departments and global organizations. Bring Together Structured and Unstructured Data: Knowledge Graph technology means being able to connect different types of data in meaningful ways and supporting richer data services than most knowledge management systems. In addition, any graph can be linked to other graphs as well as relational databases. Organizations will then use this technology to extract and discover deeper and more subtle patterns with the help of AI and Machine Learning technology. Make Better Decisions by Finding Things Faster: Knowledge Graph technology can help provide enriched and in-depth search results, helping to provide relevant facts and contextualized answers to specific questions. Knowledge Graphs can do this because of its networks of “things” and facts that belong to these “things”. “Things” can be any business objects or attributes and facets of these business objects, such as: projects, products, employees or their skills. Data Standards and Interoperability: Knowledge Graphs are compliant with W3C standards, allowing for the re-use of publicly available industry graphs and ontologies (e.g., FIBO, CHEBI, ESCO, etc.), as well as the ISO standard for multilingual thesauri. AI Enablement: Data from unstructured data sources up to highly structured data, can be harmonized and linked so that the resulting higher data quality can be used for additional tasks, such as machine learning (ML). Knowledge Graphs are the linking engine for the management of enterprise data and a driver for new approaches in Artificial Intelligence Knowledge Use Cases – Value Across Verticals Pharmaceutical Industry: Boehringer Ingelheim uses the extensive capabilities of Knowledge Graphs to provide a unified view of all their research activities. Telecommunications: A global telecom company benefits from the power of Enterprise Knowledge Graphs, helping to generate chatbots based on semi-structured documents Government: A large Australian governmental organization provides trusted health information for their citizens by using several standard industry Knowledge Graphs (such as MeSH and DBPedia etc.). The governmental health platform (Healthdirect Australia) links more than 200 trusted medical information sources that help to enrich search results and provide accurate answers. IT & IT Services: A large IT services enterprise uses Enterprise Knowledge Graphs to help them link all unstructured (legal) documents to their structured data; helping the enterprise to intelligently evaluate risks that are often hidden in common legal documents in an automated manner. Digital Twins and Internet of Things. The Internet of Things (IoT), considered as a graph, can become the basis of a comprehensive model of physical environments that captures relevant aspects of their intertwined structural, spatial, and behavioral dependencies. It can support the context-rich delivery of data for network-based monitoring, provide insight into customer pain points, and control of these environments with and extension to cyber-physical systems (CPS). Examples of this application are electric utilities with their extensive interconnectivity (wired and wireless), cyber security mandates and rich digital information (asset and customer) Better Understanding of the Individual. Whether as a human resource tool or a customer service enabler a Knowledge Graph centered on the individual can connect data from across multiple sources (training, reviews, purchases, returns) and enable insights and recommendations for individuals as well as organizations. Incorporating Knowledge Graphs In Your Organization If one or more of the following scenarios sound familiar, then a Knowledge Graph can provide value: There are communication breakdowns across domains, as your departments have different views on things, across organizations, as different departments have their own language, and because the nomenclature has changed, and things today are named differently than a couple of years ago. Getting the answer from existing systems is time consuming or fails because: there are so many systems, but they do not talk to each other, they all have different data models, and you need help to translate between them, data and information reside in multiple sources structured and unstructured (Excel, SharePoint, Word, Power Point, PDFs, CRMs, intranet sites) with no defined connection, you need experts to help wrangle the answers out of your systems, and you always use Google instead of internal tools to find things. You often keep wondering if you are missing insights: because you have documentation that relies on subject matter experts to infer meaning and insights, or your artifacts sometimes have obscure or inconsistent statements that are open for interpretation, or making the connections across domains, documents, and individuals is challenging or not feasible. About Cedrus: Knowledge Graphs are among a number of tools that Cedrus Digital utilizes in the AI transformation journey for companies of all sizes. If you’d like more information on Knowledge Graphs – including technology, life cycle, and how it can add value to your organization, please feel free to contact us Martin Cardenas

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Pages (51)

  • 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 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). We also deploy 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 Our work on AWS MSK for Kafka LEARN MORE OUR AWS OFFERINGS 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 Out of gallery READY TO GET STARTED ? Get in touch with us! We're thrilled to answer your questions and help you define a vision. Contact Us

  • PUBLICATIONS | Cedrus Digital

    PUBLICATIONS Get in-depth perspective on our offerings, implemented solutions, areas of expertise, and technologies. What would you like to learn more about? CLOUD SECURITY CLOUD NATIVE DIGITAL TRANSFORMATION AI IoT DESIGN EVENT STREAMING CLOUD SECURITY Netskope Assurance Review DOWNLOAD Netskope Cloud Security Acceleration Workshop DOWNLOAD Netskope Platform Acceleration DOWNLOAD Netskope Managed Service DOWNLOAD Netskope Private Access DOWNLOAD Blue Coat Migration to Netskope for Web DOWNLOAD Cloud Security IAM for RPA and bots DOWNLOAD Road to CASB: Business Requirements DOWNLOAD CASB Growth, Trends, 2018 Forecast WATCH CASB , Cloud Identity, and IAM in your Cloud future WATCH Journey from Appliance-Based to Cloud Software Perimeter (SASE) WATCH CLOUD NATIVE ACBL Case Study DOWNLOAD Apache Kafka in Plain English DOWNLOAD How API Connect will Coexist with Mesh-Istio WATCH Migrating to Amazon MQ LEARN MORE PCF Migration - Journey to OpenShift and the Cloud WATCH DIGITAL TRANSFORMATION AWS Overview DOWNLOAD IBM Cloud Private Webinar WATCH ARTIFICIAL INTELLIGENCE (AI) How IoT and AI are Revolutionizing Business DOWNLOAD Benefits of Using AI to Automate and Leverage Unstructured Data Intake WATCH Chatbots for Highly Regulated Industries DOWNLOAD Smart Data Intake Solution DOWNLOAD The Art of Automating Healthcare Provider Data Intake AI & RPA DOWNLOAD The Ultimate Guide to Enterprise Content Capture DOWNLOAD AIOps Roadmap – Adding Intelligence to Operational Automation WATCH INTERNET OF THINGS (IoT) Are you ready for the Internet of Things? DOWNLOAD AWS IoT Webinar WATCH IoT and the New Path to Business Webinar WATCH DESIGN Design Offerings DOWNLOAD EVENT STREAMING Leveraging Kafka Event Stream WATCH Our work with AWS MSK for Kafka LEARN MORE LEARN MORE Take a look at our YouTube channel Catch up with us on our Blog Find us on LinkedIn Contact us

  • AWS MSK Webinar | Cedrus Digital

    Amazon Web Services MSK Webinar with Cedrus Check out our webinar on AWS MSK services for Kafka! AWS MSK + Cedrus Webinar Sept 23, 2021 Cedrus Amazon MSK Analysis Experience how Cedrus has helped some of the largest companies in the world transform with Event driven Architecture. Cedrus will help uncover the benefits of Kafka and Amazon MSK with a complimentary EDA workshop. Contact Us AWS Marketplace WANT TO LEARN MORE ? Get in touch with us! We're thrilled to answer your questions and help you define a vision. Contact Us

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