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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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  • Governance | Cedrus Digital

    GOVERNANCE, OVERSIGHT, POLICY, AND CAPABILITY IN CLOUD SECURITY Two converging technologically transformative forces are changing the landscape of IT and security. The world has been steadily embracing user choice – devices, flexible locations and work schedules, and Software-as-a-Service (SaaS) productivity tools. Businesses must adapt or go extinct. The world is shifting application services and workloads to public cloud Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS); enabling speed, flexibility, and reduced cost. As a result, business cloud footprints are growing exponentially and the line between “inside” and “outside” is blurring. Our increasing interconnectedness fosters collaboration, but the ease and speed to share data without proper security checks has created new risks and challenges. Further, because some businesses are making mistakes with personal information, the public has demanded that third party oversight exist to systematically enforce good behaviour with personal and financial data of all types. Now, regulatory compliance mandates are increasing in both number and complexity at national, state, and local levels. Cloud security must be addressed in a way that enables user collaboration and the inherent agility and value of the cloud; while providing enterprise-grade security to “the right things” at “the right time.” It must do this while simultaneously meeting or exceeding regulatory compliance requirements. As more data is moved onto or into devices, applications, and data centers not under direct control; Information security, risk management, and cyber security teams must mitigate risk in new ways while enabling the business to move swiftly and remain competitive using the cloud. We’ve seen many cloud security vendor products that offer solutions to these problems. However, there is a limit to where the vendor can provide guidance to your specific business risks and compliance requirements. Many of these best practices come in the form of policy-specific packaged configurations such as “GDPR Compliance” policy or “PCI DLP Profile,” which are extremely valuable, yet incomplete. Businesses need to establish guardrails on how cloud services should be used and managed, including identity, unstructured data, and enterprise on-premises integrations to name a few. To do this effectively, cloud risk governance groups must be established to carry business level guidance to the technical world of cloud security and periodically measure effectiveness of controls. CEDRUS CAN HELP 1. Cloud Security Advisory: Cedrus assists businesses in building cloud security governance, oversight teams, and the necessary processes for cloud adoption and usage. We help to clarify guidance and capture minimum requirements such as: Legal considerations in engaging cloud a vendor such as terms and conditions, license agreements, or data jurisdictions Compliance considerations in engaging a cloud vendor such as data center compliance, Payment Card Industry Data Security Standard (PCI DSS) compliance, or Service Organization Control (SOC) reporting Information Security considerations for engaging a cloud vendor such as security controls and encryption capabilities Information Protection considerations for engaging a cloud vendor such as data ownership or data deletion upon service termination IT department architecture considerations such as protocol requirements or integration requirements Ready to Get Started? Contact us! 2. Cloud Security Standards: Cedrus assists businesses in the creation of reference documents in the area of cloud security standards and guidelines to be used for internal project teams embarking upon initiatives leveraging the cloud. ​ Step 1: Perform a Gap Analysis that provides evaluation of existing cloud-relevant Information Security Policies and Standards to incorporate into the process, including: Acceptable use Identification and Authentication Application Security Data Classification / Handling Vendor Risk Management Encryption Logical Access Control Compliance Step 2: Create a Guidelines and Standards Document that will outline the security governance criteria including, but not limited to, general standards such as: Regulatory Mandates Enterprise Risk Levels Vendor Trust Criteria Data Classification and Data Leakage Prevention (DLP) Identity and Access Management, Access Control, Privileged Access Encryption and Key Management Mobile and Endpoint ​ Ready to Get Started? Contact us! 3. Cloud Security Capability Assessment: Cedrus also assists businesses with cloud security capability mapping, ensuring that control gaps can be identified and closed and that best practice security approaches for operating in the cloud can be met. We partner with Cloud Security Alliance (CSA) and leverage the Cloud Controls Matrix (CCM) and our consultants all hold the CSA Certificate of Cloud Security Knowledge (CCSK). [CSA LOGO] Step 1: Capability Map - In this process the Cedrus consultants analyze the existing cloud security technology capabilities as compared to the relative guidance provided in the CCM. We assist in determining: Does a capability exist where a control is recommended by CCM? Do any gaps exist where a control is required by business policy? If capabilities exist, are they implemented and managed? Are there redundant tools/solutions in any area? Are tools/solutions cloud ready and/or cloud aware and supported/supportable? Step 2: Create a recommendations and roadmap document to outline recommended solutions to control gaps or under-configured solutions along with a suggested timeline and budget to implement the controls. ​ Ready to Get Started? Contact us!

  • Infrastructure Protection | Cedrus Digital

    CLOUD INFRASTRUCTURE-AS-A-SERVICE (IaaS) PROTECTION AND SECURITY (AWS, Azure, and GCP) Cedrus maintains relevant security skills and certifications in popular CSP technology stacks like AWS and Microsoft Azure. Through our technical knowledge, and information security experience in the enterprise, Cedrus can assist your business in securing your cloud Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) subscriptions and configurations. In many ways, the Cloud Service Providers (CSPs) are more secure than almost any business in the portions that they are responsible for, but being clear on the parts that your business owns and how to apply security properly is critical. As businesses relinquish control of physical data centers and system hardware, information security responsibilities in some areas are amplified because services that may now be accessible from anywhere have an increased level of exposure risk and more threat / attack vectors. In addition, each of the main Cloud service providers have slightly different naming conventions to describe similar capabilities and requirements. At Cedrus, we understand the Information Security team’s desire to apply best practices as the vendor level, while also adhering to a centralized and standard set of controls across vendors. A good reference point is the Shared Responsibility model. The model below describes this in a general way, the major CSPs all have good documentation on the details and benefits of their particular approach to security. ​ CEDRUS CAN HELP 1. Architecture and Security Configuration Assessment: Organizations are moving to the Cloud at a pace that does not align with traditional Information Security processes. Cloud security is a new and unique domain. Additionally, AWS Cloud Security experts are rare and hard to retain. Organizations often inherit AWS accounts from acquisitions or are home-grown by line of business. Cloud security requires specialized skills and knowledge to ensure that risk is mitigated properly. Many organizations struggle to dedicate the appropriate time and resources to these critical projects. ​ The service offering will focus on the following security epics: Identity Access Management (IAM) Data protection Infrastructure protection Detective controls Incident response The assessment process is conducted in three steps: Discover : Inspect AWS accounts using industry tooling and interview account owners to identify vulnerabilities. Assess : Review the security policies and procedures of the existing accounts against the AWS security pillar of the Well-Architected Framework and the Center for Internet Security (CIS) recommendations. Report : Document a remediation plan and an approach for how to ensure that future AWS accounts are configured properly. ​ Ready to Get Started? Contact us! 2. Container Security: More information coming soon! ​ Ready to Get Started? Contact us! 3. Cloud Security Posture Management (CSPM): After an initial assessment of your security configuration is complete and remediation roadmap/plans are in place, Cedrus can enable an ongoing monitoring solution to make sure that your infrastructure configurations do not slip out of compliance. This is a cornerstone for providing security on any CSP platform for any business leveraging IaaS or PaaS, but most valuable when: The business has a multi-cloud strategy and wants to enforce similar controls across providers The business has limited cloud native security experts to proactively review configurations and local events The security posture scanning is leveraged in conjunction with: Data Leakage Prevention (DLP) to prevent leaked keys from attackers Shadow IT discovery capability to detect new subscriptions and ensure that they are brought into the standard approach to compliance Cloud Identity services for Federation and Privileged Access Management (PAM) ensure that the CSP admin accounts are protected and actions are monitored In addition to partnerships with the major CSPs, Cedrus partners with Netskope and Okta cloud security solution vendors to provide robust enterprise-ready cloud security for IaaS, PaaS, and SaaS. Learn more about our partnerships here. ​ Ready to Get Started? Contact us!

  • PCF to OpenShift Migration | Cedrus Digital

    Pivotal Cloud Foundry (PCF) to OpenShift Migration 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. Why migrate from PCF? Roadmap Product roadmap is unclear If you have to migrate, why not migrate to freedom? Kubernetes has a solid, clear direction forward Cost Kubernetes is free and open source Managed Kubernetes solutions are expanding and are cost-effective Capabilities Full-functional, secured, well-governed and mature platforms available such as OpenShift Kubernetes can fully manage persistence, allowing for stateful apps Portability Kubernetes is dominant and continues to improve rapidly Kubernetes apps and configuration can be easily moved between clusters/providers Kubernetes easily runs on VMs, Metal, and across IaaS providers Why Red Hat OpenShift? 1. Enterprise-ready PaaS functionality Your business is not platform building Outstanding dashboard and CLI to manage and monitor Deep integration with public clouds simplifies operations CI / CD built in - One line/page creation of apps from source to deployment Operator frameworks automate solutions Rich automation - for days 0, 1, and 2+ Built in image and operator repository 2. Security-first engineering No root usage in containers, RHEL / Fedora foundation S2I builds are better than Docker builds Strong RBAC isolation Built in adapters to connect to your identity provider(s) 3. Fully open source (Apache 2.0) OKD, the upstream version is available for free Why Cedrus? Over 20 years of experience in managing, architecting, and implementing successful platform migrations Dedicated Integration, Cloud, and Security practices Deep PCF experience Experienced Kubernetes team Red Hat Apex partner with expertise in Red Hat technologies including OpenShift, RHEL and Ansible Migration Methodology: 3 Phases Infrastructure / Platform Assessment and Implementation Enable visibility of legacy platform and all applications Identify and plan for all platform gaps and application dependencies Map functionalities to OpenShift early in the process CI / CD Pipeline Analysis and Implementation 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 Operators Adapt pipeline stages to leverage OpenShift standards like Kubernetes Helm Charts and Operators Automated Code Migration Refactor the first application manually Produce code refactoring Run-Book Automate code refactoring for the rest of the applications Cedrus OpenShift Migration Tool The tool is a NodeJs , Yeoman generator that sums up the aspects, infrastructure needed and logistics of any application deployed on PCF in seconds. It scans the PCF platform through API's and the configuration files in the PCF applications against pre-defined and customizable rules. The customizable output provides an accurate summary for every application including details such as: Services in use Autoscaling Properties Instance profile (Dev, QA, Pre-Prod, Prod) Stack and Buildpack detail Shared Libraries summary Java and NGINX plugin and dependency details Customizable attributes as needed per unique migration PCF Migration - Journey to OpenShift and the Cloud WATCH PCF Migration Red Hat Webinar WATCH OpenShift and AWS blog READ 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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