• Scripted Automated Load Balancing Setup
  • Auto Deploy Labs
  • Webs & VMs
  • VMs & Scale Sites
  • Auto Domain Joining
  • Scheduled Scripted Start & Stops for Cost Saving
  • Various Types of Azure Deployments, Web Services & Web Jobs
  • Service Bus
  • Azure Search
  • Document db
  • Porting Existing Services to Azure

Infrastructure as a service (IaaS) is an instant computing infrastructure, provisioned and managed over the Internet. Quickly scale up and down with demand, and pay only for what you use.

IaaS helps you avoid the expense and complexity of buying and managing your own physical servers and another datacenter infrastructure. Each resource is offered as a separate service component, and you only need to rent a one for if you need it. The cloud computing service provider manages the infrastructure, while you purchase, install, configure, and manage your own software—operating systems, middleware, and applications.

  • Lab Deployment
  • Azure DSC
  • Complete AD Hosting in Azure
  • Scripted Virtual Network
  • Auto Deployment
  • Scripted VPN between Virtual Networks
  • Scripted Isolated Lab Deployment
  • Connect In-house infrastructure with Azure through Secure Channel
  • Edit Security & Peak Load Balancing Scenario where by Infrastructure goes to the Cloud if needed.
  • Big Data and analytics technology that you can use to solve issues and profit from the opportunities that your enterprise faces.
  • No matter your industry focus, HPE Big Data technology helps you become more efficient, increase profits and better adapt to a changing environment.
  • It does this by extracting contextual meaning from any data source, analyzing data in real-time at extremely high volumes and quickly delivering intelligent actionable information.
  • Big data changes business processes simply by delivering actionable intelligence which then defines the action.
  • But that’s not the only way big data is used to streamline and improve processes, nor is a change in processes the only change businesses will likely affect.
  • On-demand computing (ODC) is an enterprise-level model of technology and computing in which resources are provided on an as-needed and when-needed basis.
  • ODC make computing resources such as storage capacity, computational speed and software applications available to users as and when needed for specific temporary projects, known or unexpected workloads, routine work, or long-term technological and computing requirements.
  • Web services and other specialized tasks are sometimes referenced as types of ODC.
    ODC is succinctly defined as “pay and use” computing power.
  • It is also known as OD computing or utility computing.
  • Storage optimization is a process, supported by tools, that uses tiers of storage to meet service level requirements at the lowest possible cost.
  • It is not an event. It is an ongoing effort to maintain the optimized placement of business data objects across the storage infrastructure.
  • The cost of maintaining optimized storage has two components.
  • First, there is the cost saving associated with using storage with a lower unit cost and inherently lower performance, to store data objects that do not require extremely high performance. Second, there is the cost of maintaining the optimum placement.
  • The balance between these two costs defines how successful your optimization effort is.
  • From the standpoint of a high-performance computing (HPC) cluster, the goal of big data job scheduling is to process and complete as many jobs as possible.
  • On the surface, this goal sounds like its transaction processing counterpart, but there are definite differences.
  • Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data.
  • You can create data integration solutions using the Data Factory service that can ingest data from various data stores, transform/process the data, and publish result data to the data stores.
  • Data Factory service allows you to create data pipelines that move and transform data, and then run the pipelines on a specified schedule (hourly, daily, weekly, etc.).
  • It also provides rich visualizations to display the lineage and dependencies between your data pipelines, and monitor the pipelines from a single unified view to easily pinpoint issues and setup monitoring alerts.
  • Create, schedule, and manage data pipelines
  • Visualize data lineage
  • Connect to on-premises and cloud data sources
  • Monitor data pipeline health
  • Automate cloud resource management
  • We are using Python and R for our machine learning projects.
  • Our models use various advanced libraries like Caret, Keras and TensorFlow.
  • We extract meaningful information from user input using natural language processing techniques.
  • We are using OpenCV and Tesseract for real time image computer vision and optical character recognition.
  • Tesseract is an optical character recognition tool which can easily recognize text from images.
  • Companies rely on predictive analytics to plan or improve the customer experience.
  • Take Netflix and Amazon for example. Netflix learns which movies viewers are likely to enjoy and Amazon predicts what a customer will buy—even going as far as to patent “anticipatory shipping,” which would deliver packages to a geographic region before a customer buys them.
  • In its multiple forms—predictive modeling, decision analysis and optimization, transaction profiling, and predictive search—predictive analytics can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.
  • These techniques can provide managers and executives with decision-making tools to influence upselling, sales and revenue forecasting, manufacturing optimization, and even new product development.
  • A data integration system provides a uniform interface to a multitude of data sources.
  • The data may have different data types, follow different statistical distributions, and more problematically, possess different semantics.
  • Nevertheless, when given a user query formulated in this interface, the system accesses and combines data from all the necessary sources to answer the query.
  • By automatically learning mappings between source schemas and the mediated schema, machine learning can eliminate much of the development effort involved in building data integration applications. After a subset of data sources has been manually mapped to a mediated schema, machine learning programs use the information in these data sources to propose mappings to subsequent data sources.
  • Sentiment analysis deals with the computational treatment of opinion, sentiment, and subjectivity in text.
  • It intends to ascertain the attitude or opinion of a speaker or writer with respect to a certain topic or target.
  • The attitude could reflect his/her judgment, opinion or evaluation, his/her affective state (how the writer feels at the time of writing) or the intended emotional communication (how the writer wants to affect the reader).
  • Furthermore, it should be noted that in this context ‘subjective’ does not mean that something is not true.
  • In sentiment analysis, subjective language used to express private states in the context of a text or conversation.
  • We developed a cloud hosted Bitcoin exchange platform for our European and Brazilian customers.
  • The users of the Bitcoin exchange platform store their Bitcoins in our secure wallet.
  • The platform allows its users to trade Bitcoins safely and securely.
  • The platform has an integrated banking and card payment system.
  • Create your own Decentralized Autonomous Organizations and beat the competition to it.
  • We will create Smart Contracts for your autonomous organization.
  • Our platform will enable you to Trade and Exchange Ethereum Tokens.
  • We can help create your private Blockchain.
  • You can also launch your own tokens on the public Ethereum Blockchain.
  • Use cloud technology for your application to create scalable and secure applications.
  • Security has always been our first priority especially when it involves financial transactions.
  • We will provide multi-signature wallets for maximum security.
  • You can use Blockchain for transparency and traceability of the transaction and records.
  • Using our cold storage mechanism, the users can keep their reserve coins offline.
  • Blockchain technology is being applied in Banking Industries to secure banks from fraud and to help reduce intermediaries.
  • Blockchain can also be used in Money Transfer to avoid fraud, cyber-attacks and operational errors.
  • Blockchain can also be used in Financial Services for providing digital identity, smart contracts, clearing and settlement.
  • Blockchain can also be used in Insurance Industry for e.g. for insurance validation and shared record keeping, complex commercial claim handling and settlement.
  • Blockchain technology can also be used in digital voting systems for fair and transparent elections or referendums be it a nation or a small community.
  • Blockchain can also use in various companies for Identity management and other immutable ledgers.
  • Blockchain can also be used in loyalty and rewards programs for both employees and customers.