Saturday, 15 February 2020

What are the fundamentals of Cloud Computing and Data Science?

         

         Data science refers to a collection of related disciplines focusing on the use of data to create new information and technology. It provides useful insights for better decisions. For ex, big data overcomes the challenge of analyzing the huge volume and modern data generated at high speed. In the real world, computing devices such as cellphones, security cameras are constantly generating data and connected to the internet, also known as IOT is ever-growing. Computers can make decisions based on trusted algorithms to make accurate predictions. Data analytics is a more enhanced way of taking advantage of exponentially increasing computing power and storage capacity. You need the basic knowledge of statistics to be a successful data scientist. Basically, the data industry is driven by IT in the languages like python, R which comes with powerful libraries that implement statistical functions and visualization features. The programmer or data scientist automate the necessary tasks and focus on solving large problems. Distributed file system like Hadoop and distributed processing like Spark plays a critical role in big data that enables you to make informed decisions. Machine Learning helps to detect data patterns and make better predictions about a dataset. For ex, In fraud detection, machine learning dramatically reduces the workload by a significant number of data points and presents only the suspicious candidates. The visualization tools can greatly enhance the presentation. Data scientists need to specialize in core job duties in particular area.
          Data science requires support from cloud computing and virtualization for the ever-increasing size, speed and accuracy requirements for the data sets we have to manage. Cloud computing provides the scalability requirement for computing resources. Actually, the cloud provides the processing power and storage space. The software application connects virtual machines through a high-speed network and implements distributed file and processing systems. Hadoop and Spark are the key elements that build on virtual machines. It solves data science problems by connecting the specific data science application. Cloud computing, virtualization, machine learning, and distributed computing are technologies for data scientists to do their job effectively. Proxmos is easy to install for cloud computing and virtualization to build your own cloud and configure the software. Weka is a machine learning tool that allows users to run various machine learning algorithms in a GUI environment.

Fundamentals of Cloud Computing: If you want to familiarize yourself with Azure computing, first you need to familiarize yourself with cloud computing as a whole. There are 3 types of cloud computing. Those are,
  1. Public Cloud
  2. Private Cloud
  3. Hybrid Cloud
       When we talk about the infrastructure, you need to know the infrastructure deployed in your company and you need to manage the server, hardware, services, firewalls managed in your
organization by internal administrator who is responsible for the functions and functionalities for the user. The user consumes the services and you need to update or upgrade and manage the hardware that services live on. In a private cloud, the user could be an administrator who has a portal based environment from which they manage the environment, provision servers, deployed applications, websites, and all the things. It depends on the software that manages the private cloud and that exposes all the functionalities in the portal. For ex, the System Center 2012 by Microsoft provides the private cloud infrastructure. It is typically a private data center so that you will be responsible for hardware, software, and network services. The vendor is responsible for most tasks that are performed in the public cloud like Microsoft Azure, Google public cloud, AWS. It uses a leasing base model which is basically pay as you go or use infrastructure that you consume resources of workloads, applications and services. The usage can be the data stored in the cloud infrastructure and services offered by virtual machines. The advantage of public cloud infrastructure is that you can deploy a new application or server at a very low cost and you don't need new hardware to support the additional infrastructure. Ultimately, it reduces the capital expenditure of the company. The Hybrid cloud is the mix of public and private solutions where you can have your own internal private data center, store workloads with some services, and applications into the public cloud. It is more complex to manage because you have to manage both environments with coexistence.

Cloud Computing Services: It is a collection of remote servers connected via computer networks available through internet. Virtualization implements cloud computing. It uses the Hypervisor operating system on which many OS can be installed like Windows and Linux. You can fire up virtual machines and leverage vast resources of cloud provider when the business operation grows gradually and exponentially. It is the flexibility to grow your infrastructure quickly if necessary.  Cloud computing companies specialize in managing server farms and know-how to maximize the profit and minimize the expenses. There are 3 major deployment models of cloud. Those are,
   1. Infrastructure as a Service(IAS) - The computer lab is an infrastructure that you are trying to use as a service. For ex., If you want 10 PCs, you can use AWS EC2 and start 10PCs and put them in the same network. Now, this is our computer lab.
   2. Platform as a Service(PAS) - It is the platform to run your code. You can just go to the cloud and tell them which compiler and interpreter you want and can run it. The cloud IDE can used to write and run the code.
   3. Software as a Service(SaaS) - It is self-explanatory. For ex, the google docs and dropbox which gives free storage and this is a cloud service software for your purposes.

Application Migration to Cloud:  A successful migration of the large portfolio requires a couple of things. Those are,

    * Think and Plan strategically and,
    * Rapidly iterate through feedback loops to fix the things that are going wrong

      But, there are a lot of things to consider when migrating to cloud. It includes application architecture, the ability to scale-out, distributed nature etc., When we are migrating the applications to the cloud, we are getting a new architecture that's going to have different properties or different characteristic than traditional systems. The advantage of cloud migration is the ability to do a active-active architecture. It means we run the application in real-time at the same time. One application takes over the other if there's a failure. Here, we are automating the things and the goodness of being in the cloud is worth to the business. The application migration are necessary, because
   * you are selling to the stakeholder that are funding to the cloud migration.
   * It makes the business more agile and delivers the value.
   * We are understanding the applications in wide for the specific needs of the application and looking at the general consensus.
   * We are modernizing the things in moving the database models, technologies, improving the security and governance and leveraging the systems whatever the purpose we need.

The important steps in Cloud Migrations are,                                                                           
                                           
Ultimately, there is a bit of trial and error, so set the operation processes and continuous improvement.

Data Migration to Cloud: Data is the highest priority when migrating to the cloud. Basically, data is the business and it is everywhere in enterprise. The data is killer application of cloud computing. We are migrating to the cloud and finding more values in new ways in innovations through running databases, big data systems, predictive analysis, and AI based systems in the cloud. So, the data selection is a critical process to understand which database is bound to which applications, what they're doing, security issues, compliance and performance issues that leads to success. In the Business case, migration, testing, and deployment are the understanding of data. You need to look at the applications that depend on data and do the deployment. Ultimately, the goal of leveraging data is to lowering operational costs, integrating existing data silos to make different databases communicate one another as a single dataset and influence actions and outcomes but not just data so they have the information they need to run the business better. We are not going to move every piece of data that exists on-premises into the cloud. We may move 70% of it and we have to deal with integration with on-premise data stores and those that exist in the cloud. So, make sure to build a solid architectural foundation for success when considering data, avoid duplicate data and data silos. In a real-world scenario, you need to consider the following things when you migrate to cloud,
 * It is necessary to understand the total cost of ownership(TCO) for the first year, second year, five years etc., It includes the TCO for applications, for databases, for cloud instances and ROI. The top 5 TCO/ROI are,
   - Value of Agility
   - Cost to retire selected applications, infrastructure or data centers
   - Changes required to maintain a service level
   - Software costs
   - Organizational transformation costs
 * Ensure that the solid business case exists before the migration can begin and how the technology going to be applied
 * The value metrics or value points that need to be determined like including the agility, compressed time to market, cost savings etc., by which you will be measured against the total cost






Friday, 31 January 2020

What is cloud computing and CRM for client app?

     

       Cloud Computing is a remotely hosted platform. It has the infrastructure, platform, and application. The infrastructure includes the computer, storage facilities, and network. The network could be centrally located or spread out internationally. On that particular hardware, there is a layer called platform. It is all about the object storage, identity management, run time environment for programs, databases etc., For ex, the various kinds of hosting platforms where you have the basic storage, databases and run time engines etc., There are various applications running on the software side. The platforms and infrastructure are managed by centrally provided companies like AWS, Microsoft Azure etc., Actually, you are logging in to the application through servers by desktop, laptop, tablets and doing your work. Everything can be installed on the cloud and not installed locally. This is called cloud computing.



The sales process in CRM can be defined as,

                        Generate   ------------>  Qualify  ----------------> Close

Qualifying Leads: For a long time, B2B businesses (not just SaaS businesses) have been using the qualifying leads. By qualifying leads, a business makes the sales process more efficient. The sales team highlighting the leads that have the highest likelihood of converting to customers. It is determined by two factors
  1.  Firmographics and,
  2.  Interest
        Firmographics measure the business characteristics of a lead. How big is the company they're with? what is their job title? what industry they are in? Qualifying firmographics hones your sales efforts on your target customer profile. Interest measures the lead's interest in buying your product. The more they do, the more interested they are and more likely they will buy. There are 3 types of leads to focus on. Those are,
    1. Marketing Qualified Leads(MQL)
    2. Sales Qualified Leads(SQL)
    3. Product Qualified Leads(PQL)
     MQL factor in firmographics and interest. They have shown little interest(eg., signed up the opt-in)to marketing initiative and some kind of firmographic criteria. These kinds of leads deserve attention but are not quite ready for a personal touch from a salesperson. SQL has a deep interest and taken actions that justify attention from a salesperson. Typically, SQL has visited your website several times, downloads the whitepaper or requested a demo. In the modern world, the free trials and freemium has changed the deep interest of the qualifying lead. But, people aren't going to pay for a product until they've seen value on it. PQL shows true deep interest by,
   * Using the Product
   * Hitting First value
    Traditional qualification criteria include downloading an ebook or visiting pricing page activities that imply an interest in your offering. The user set-up a trial so they could give your product go. From there, some will love with the features and insightful methodology on their own terms. The ones that continue the setup process for your product end-up hitting the first value. This is how to distinguish themselves as truly interested. It means the lead has found value from using the product and more likely to keep wanting to use it. PQLs are more likely to be long term successful customers.

Business Setup for PQL: To build a proper PQL process, you need to define the criteria that will make someone product qualified and have set of guideline for turning these leads into paying customers. There are 6 steps for setting up PQLs. Those are,
1. Understanding Activation(Rate) and Measurement(Score) - the two key PQL measurement
2. Set up a system for keeping tabs on product data
3. Define Activation Criteria for Product
4. Create engagement scoring engine
5. Rank Activated trials by engagement
6. Make sure sales team access to the data

    There are few steps a new user needs to do to get set up and get the value of your product. They are typically product-specific. For ex, In the modern SaaS company with GSuite Plugin for email collaboration, the activation rate is the percentage of steps completed. If the account or user has done 2 out of 5 steps, they are 40% activated. Once the account is activated, you can dig deeper into their PQL status by looking at their engagement score. This is all about the events or actions a user can take in your product. There are several things one might do in the SaaS product like creating a report, check the payment systems, etc., Suppose you have a user who sets up a trial, he could give your product go. You can figure out 3 or 4 actions that allow a new account or user to experience "first value"? These actions are the activation checklist. Because you are tracking your product data and you'll be able to track activation progress of your accounts. The engagement scoring engine allows you to give each of your users and accounts an engagement score based on how much they use your product and what features they are using. Once you created the engagement scoring model for your product, you need to compare and rank them.
         When the customer asks how they should be designing their PQL process, the first question to ask yourself is,  How complex is your product? More simply, how hard is it for a new user to self-serve their way to first value? It means manual support of intervention from your team. Simple products have a free trial and freemium model as well. Intermediate models are complex than simple, but there is an opportunity for a fair percentage of users to self serve the way to value. The complex products require manual support to get to the promised land. It requires technical implementation, access to data or tools from other departments or deeper domain knowledge for a user to get value. Typically, the complex product has a free trial period, but they can only sell to customers who go through a sales demo with a more hands-on approach.

CRM Basics:  There are 4 basic features in CRM. Those are,
         1. Marketing,
         2. Sales Force,
         3. Customer Support and
         4. Service Automation for internal service
     In business, you need to generate leads, prospects and turn them into customers so that the business will grow in that way. The leads can be generated through online, offline, websites, cold calls, emails, social media etc., How does the business nurture the lead? When you have the lead, the marketing automation sends some promotional material and you can program your emails for the automated campaigns. First, The qualified lead is to be converted. It is the transition from the formal sales process. The qualified lead needs to assign, follow up, gauge interest, gauge intent and identify the opportunities. The converted lead considered to be a sales prospect. Sales are the strategy to exchange goods. At any given time, the multiple prospects will be in different stages. You need to define your sales processes like stages, progress, and the probability of success.  The sales funnel, deals and tracking the sales are managed in the sales force automation. The prospects and stages are defined as a sales pipeline for the business. In the contact center customer support, you will be able to assign various cases to representatives and assign cases to various people to give support and decide when the services are going to happen. In service automation, you can create projects, calendars and automate the features of messages, reminders, and alerts. So, Design something that works for your business. Here is the sales cycle that must be refined over time.
     One of the main utilities in CRM is Analysis. You will be able to create the reports from various sources like leads that are to be converted, budgets, sales, number of support service calls, calls answered, and cases solved. It is important to analyze all the data and construct business decisions out of the state. The Collaboration utility will help you to collaborate with the marketing, sales, HR and accounting Team. Also, you can send emails together or each other to work with the project. It is possible to collaborate in any office and any part of the world with remote login to that place and do the collaboration. Next, the Relational Intelligence is to understand and analyze each and every customer with the number of deals doing with them, how much we spend for our product, product preferences, material they are supplying etc., From the data, you will be able to understand the relation with the customer or vendor and able to predict the future from the past history. Lastly, the customers are coming from various channels like online, offline, telephone etc.,  All the input channels can be collected into a central database that can be categorized and assigned individually. This is called multiple channel integration. For ex. the cloud based SaaS agile CRM automate sales, marketing and service in one platform.
      The CRM works in a way that the leads are coming from verbal communications, telephone marketing, emails that are send to appropriate data classification. First, it is send to composition and insertion of organizational database that will classify into various categories. Once the data has put into organization database that data gets analyzed and disseminate into various departments like the support queries goes to support, leads goes to sales, campaign related queries goes to marketing and management related queries goes to management.


Tuesday, 14 January 2020

What are the Important characteristic of Artificial Intelligence(AI)?

   
         
      AI is the development of methods and algorithms that allow computers to behave in an intelligent way. AI was introduced to the scientific community in 1950 by ALAN TURING in computational machinery and intelligence. Turing explained in his article that the machines can think and argued in favor of intelligence in machines that marked the interaction between AI and psychology. He focused on analysis, problems and mentalistic terms. This intended to eliminate the distinction between natural intelligence and artificial intelligence. He has contributed the design of the first computer capable of playing chess and the establishment of the symbolic nature of computing. In 1962, McCarthy and Raphael designed and constructed the mobile robot called "Shakey" that has to challenge the real world in terms of space, movement, time, etc., It initiated the study of cognitive process discussion centered around the problems of mental and internal representation of knowledge, perception, and meaning of the problems. Raphael's basic ideas were to gather in a machine with a capacity to learn from the experience, to model, recognize the visual patterns and manipulate the symbols etc., There are some questions about the performance of intellectual work by some machines to be a truly intelligent function? Maurice Wilkes proposes an argument the need to develop a generalized learning program that enables a computer to learn in any field.
          AI can automate any mechanical processes like calculation, data storage, and processing. There are 3 basic steps in the fundamental process behind making decisions in problem-solving are,
     1. Analysis of the Situation
     2. Logical Reasoning and
     3. Decision
The application has been developed to simulate human behavior through computer systems. Human senses are interrelated with computer systems to build the applications that humans are solving every day. AI program will help the following goals like learning, perception, and problem-solving. Also, it is used in specific areas like diagnosing diseases and driving cars etc., There are 4 types of AI. Those are
    * Systems that act as Humans - For ex., Simulating the human behavior in a given environment
    * Systems that think like Humans - For ex., Machine makes think like humans
    * Systems that act Rationally - For ex., Intelligent behavior that can be created with a computational process
    * Systems that think Rationally - For ex., Focusing on mental faculties that can be emulated in computer models
Basically, we are separating the logical or human and action-oriented or cognitive oriented. And, there are 3 basic domains in AI. Those are,
    * Formal  Domains - It is intended to solve problems using different models like search models, algorithms etc.,
    * Technical Domains - This will be used in scientific-technical knowledge like medical diagnosis, robotics, expert systems etc.,
    * Cognitive Domains - Here, we try to understand the functioning of the human mind and its cognitive functions like reasoning, hearing, talking etc.,

Characteristics of AI: The behavior of the program is not explicitly described by the algorithm. The sequence of steps is influenced by that particular problem present and the program specifies the sequence of steps necessary to solve a given problem. It finds the own way to the solution to the problem. This is called a declarative program.  On the other hand, the program that is not AI follows a algorithm that explicitly defines the rules for a given input variable in any given program. This is called a procedural program. The programs incorporate the factors of relationships of the real world and the domain of the knowledge in which they operate is called knowledge-based reasoning. In accounting, the AI collects the knowledge and more adaptive towards solving new types of problems. AI's will not work poorly structured problems and data.
     Scope:  The machines solve the problem that was not defined before. It does not solve the specific problems of the second-degree equation. It's a method that creates a system capable of finding methods to solve the problem. It goes a higher level of understanding and solves a broader variety of problems.
    Perception: The machines able to react to the environment and influence through sensors and interaction devices with outside. The perception of image synthesizers that a lot of computer communicate through spoken language on the walls and not recently has done before.
  Communication: It communicates with the computers through an assembly language or high-level specific language. AI by means of understanding the nature of language that we humans can speak particular problems. Traditional languages have not been well adapted to AI applications. AI applications are using the Java, Python etc.,
  Expert Systems: It consists of large knowledge bases created to store the information available to human experts in various fields and a series of rules manipulation expressed in the specific language. For ex, computer design, medical diagnosis, chemical engineering provides the material of highly successful expert systems.
   AI Hardware: AI technique came fast access to memory banks and they are huge compared to other types of programs. Also, they are fast manipulation of data. The more advanced the hardware, it is easier to work on AI program.
   Robotics: The science of robotics involves different AI techniques. The idea of a ready robots with the ability to learn from experience is the central theme of AI and research. The robot communicates with the natural language and must perform the task equivalent of machine and origin.

Applications of AI: AI is a combination of technique and algorithms that have the purpose of creating machines with the same capabilities that human being has. We have logical reasoning, presentation of knowledge, planning, general intelligence, natural language processing, perception, and many others. For ex, Siri from Apple social network, Pinterest and google photos are an example of AI. Everything is possible with AI. Here are the practical examples of AI,
   * In agriculture, it simplifies and accelerates decision making. Also, the best time to plant and harvest. There are thousands of platforms using market analysis on the information such as information about soil seeds, climate changes and analyzing all the information, we can predict what will happen and the best results.
   * In logistics and transportation, there are self-driving cars that can be driven by robots in the modern world.
   * In health and biotechnology, there is a faster and accurate diagnosis of health. AI helps physicians and patients have a faster and more accurate diagnosis. One of the most important aspects of decision making is the detection of different diseases or taking information from blood samples and other types of material to be analysed. AI provides key insights into the data provided by patients. By analyzing the data, we can make better and faster solutions to the existing problems.
   * In Marketing, AI makes the sales forecast and choosing the right product to recommend the particular cost into a particular customer etc., There is an excellent application in the retail sector is inventory optimization where AI forecasts incomes and determines how much input should be purchased.
  * In education, AI suggests a new course, personalize the course for optimized learning and promote education. Also, it allows us to help and build an online education system.
  * In financial services, the financial institutions recognize the risk of a customer and predict the market patterns, consequences and recommend the operations.
  * In manufacturing and supply chain, AI conducting the study of the product and parts that require maintenance. It helps the manufacturing companies about when to buy or produce as well as predicts the impacts and risks for the supplier.
  * In Banks, the personal assistance helps to perform some digital operations and answer to the questions which streamline the attention to the public.
        There are some great websites provides AI services that automate the workforce using AI tools. The AI software solves the hassle of scheduling meetings and appointments. It solves all the problems you face when you schedule the meetings and appointments to work. It uses Amy and Andrew that understand the natural language and help you to schedule, negotiate the meeting with your clients. The Octaneai makes the sales through Facebook messenger and increases your revenue that your customer will love. This chatbot kind of thing works on the abandoned messages like people purchasing the product and pitches the product. It can turn out automatically as a question that will help to turn a revenue and customer behavior for the results. The Datarobot provides complete automation for machine learning. It automatically builds and evaluate thousands of models and manage all the deployed models and data sets. The Presenceai helps B2B customers and replace the calls to text. It automates the recurring tasks like sending and booking the reminders confirmation etc., The smith is a receptionist service that automatically captures the website tag and books the leads or clients and builds the relationship with existing clients. The Codota is the ultimate java developer that completes millions of code. You will be able to code examples by click, follow standards, reduce errors in java. The Zenbo is a small robot that can speak, connect, learn, express. The vspatial is the workplace of the future for remote access of your PC and collaborate with the participants.