This solution is built as sample to detect an object from the video streams on an x64 PC. Custom Vision is an image classifier that is trained in the cloud with your own images. IoT Edge allows you to remotely manage code on your devices and run the model next to your cameras, where the video data is being generated.

You can add meaning to your video streams for detecting road traffic conditions, estimate wait lines, find parking spots, etc. while keeping your video footage private, lowering your bandwidth costs and even running offline.

For performing this activity, below modules…


In Azure Data Factory, continuous integration and delivery means moving Data Factory pipelines from one environment (development, test, production) to another. Azure Data Factory utilizes Azure Resource Manager Templates to store the configuration of the various ADF entities (pipelines, datasets, data flows, and so on).

Below CI/CD lifecycle in an Azure data factory is configured with Azure Repos Git. Lets check the steps involve in it below:

  1. Configure the Git integration whether at creation of Data Factory or for the existing one with code to configure Azure DevOps Integrations with Azure Repos. …


For gaining the maximum value of data products, they should be delivered in a timely manner. Moreover, consumers should have confidence in the validity of outcomes. By automating the building, testing and deployment of code, development teams are able to deliver more releases and reliably in a shorter time than the manual processes.

So, in short definition of CI/CD-

“Continuous integration is the practice of testing each change made to your codebase automatically and as early as possible.

Continuous delivery follows the testing that happens during continuous integration and pushes changes to a staging or production system.”

In case of…


What is Azure Virtual Machine?

Azure Virtual Machines provides the flexibility of virtualization by maintaining the physical hardware that runs on it. To meet the needs, the number of VMs that any application is using can scale up and out.

For tasks such as configuring, patching, and installing, the software dependent on the requirements and the opted services(IaaS, PaaS, SaaS).

Some examples of using Azure VM:

  • In Development or Test- To create a VM with specific configurations requirements for coding and testing an application.
  • Applications in the cloud- As the demand of the application can increase or decrease, so on the economic terms its beneficial…

Azure App service(in short): Azure PaaS service is the platform that handles infrastructure so developers can focus on the core web apps and services. It provides enterprise grade security and compliance.

The apps including Web Apps, API Apps, Mobile Apps or Function Apps(optional) runs in an App Service plan. It defines a set of compute resources for a web app to run. More than one app can be configured to run on the same computing resources in the same App Service plan.


Aim: With provided 5 csv sales files, ingress the data, transform and load into master tables(star\snowflake) and display KPIs with BI tool.

Tools Used: MySQL database docker, Metabase docker, cloud Virtual Machine, pandas libraries of python.

Analyzing provided dataset:

  1. Salesman code can repeat depending on whether its active or not active.

2. Customer code can repeat depending on customer addr1 ,addr2,email id ,phone,active


Diagram to follow:

Infrastructure requirements for this activity:

3 Ubuntu 18.04 Bionic Beaver LTS of small 2 units tag it as

  1. Kube Master
  2. Kube Node 1
  3. Kube Node 2

a. Install docker on all the three servers in preparation for standing up a Kubernetes cluster.

  1. Add docker repository GPG key using the below command:

Simulate Web Server Logs

  1. Install git : sudo yum -y install git

2. Clone github repository path on server:

git clone gen-logs-python3-master.zip


Big data in Banking

•Fraud detection: Using machine learning techniques like anomaly detection, recognize fraud real time to prevent and minimize losses.

•Minimize risk and compliance issues: Using big data can enhance model quality and analysis of risk management. Big data provides auditors new and innovative sources to broaden and deepen the search for risk and compliance issues.

  • Customer management: Using customer 360 degree view for driving sales, boosting retention, improving service, and identifying needs, so the right offers can be served up at the right time.

Big Data in Retail

  • Customer relationship management: Getting a 360 degree view of the customer that takes into account all available…

Types of Big Data Analytics

  • Descriptive: Describes the current state that answers what and when type of questions. Typically uses reports, dashboards, visualizations like charts and graphs.
  • Predictive: An analysis of historical data to predict what might happen. Yields a forecast of a probable outcome.
  • Prescriptive — This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps.

Descriptive Analysis

  • Creates a summary of historical data to yield useful insights.
  • Statistics operations like sum, average, count, percentages are used to summarize data.
  • Provides…

ISHMEET KAUR

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