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Step-by-Step Guide to Creating an AWS RDS Database Instance

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 Amazon Relational Database Service (AWS RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Instead of managing servers, patching OS, and handling backups manually, AWS RDS takes care of the heavy lifting so you can focus on building applications and data pipelines. In this blog, we’ll walk through how to create an AWS RDS instance , key configuration choices, and best practices you should follow in real-world projects. What is AWS RDS? AWS RDS is a managed database service that supports popular relational engines such as: Amazon Aurora (MySQL / PostgreSQL compatible) MySQL PostgreSQL MariaDB Oracle SQL Server With RDS, AWS manages: Database provisioning Automated backups Software patching High availability (Multi-AZ) Monitoring and scaling Prerequisites Before creating an RDS instance, make sure you have: An active AWS account Proper IAM permissions (RDS, EC2, VPC) A basic understanding of: ...
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Machine Learning Tutorial - Part:2

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Machine learning is a branch of artificial intelligence. Using computing, you will design systems. These systems to behave with AI features, from your end, you need to train them. This process is called Machine Learning. Read my part-1 if you miss it.
machine learning life cycle

The life cycle of machine learning

  • Acquisition - Collect the data 
  • Prepare - Data Cleaning and Quality 
  • Process- Run Machine Tools 
  • Report- Present the Results

Acquire Data

You can acquire data from many sources; it might be data that are held by your organization or open data from the Internet. There might be one data set, or there could be ten or more.

Cleaning of Data

You must come to accept that data will need to be cleaned and checked for quality before any processing can take place. These processes occur during the prepare phase.

Running Machine Learning Scripts

The processing phase is where the work gets done. The machine learning routines that you have created perform this phase.

Reporting

Finally, the results are presented. Reporting can happen in a variety of ways, such as reinvesting the data back into a data store or reporting the results as a spreadsheet or report.

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