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:
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…
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:
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:
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
a. Install docker on all the three servers in preparation for standing up a Kubernetes cluster.
2. Clone github repository path on server:
git clone gen-logs-python3-master.zip
•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.
Types of Big Data Analytics