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Component	Example Rate	Notes
vCPU (per hour)	$0.0413	Applies to both the primary instance and any replicas for PostgreSQL if they use the same tier.
SSD Storage (GB/month)	$0.17	Each instance—primary or replica—incurs storage charges separately.
Read Replica vCPU (per hour)	Same as primary	A replica with, for example, n1-standard-4 also pays $0.0413 per vCPU/hour.
Read Replica storage (GB/month)	Same as primary	You pay for each replica’s allocated storage.

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Component	Example Rate
vCPU (per hour)	$0.0413
SSD Storage (GB/month)	$0.17

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Feature	Enterprise Edition	Enterprise Plus Edition
Machine Type	Customizable vCPU/memory	Predefined performance-optimized
Max vCPUs	96	128
Max Memory	624 GB	864 GB
Use Case	General workloads	High-performance analytics, large-scale apps

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ARGO CD Installation Guide

Prerequisites: 
1. Basic understanding of Docker, Kubernetes, and CLI.
2. Access to a computer with an internet connection.
3. A running Kubernetes cluster.

Deploy Argo CD to Kubernetes
Create a namespace for Argo:
kubectl create namespace argocd

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Java frameworks are used to create web-based applications and software. They are templates of pre-written code that is specific to the Java programming language and allow a developer to add their own code to it as needed to develop the desired application. Think of it as the three pieces of puzzles to a four puzzle board. You add your code as the fourth puzzle and the final product is the application that you intended to develop. Now that we know what a Java framework is, it’s time to know why we use it.

Why use Java frameworks?
The simple answer is to make the developer’s work easy. It takes days to develop an
application and developers don’t want to spend hours writing the same chunk of code again and again. Additionally, extensive frameworks provide a secure application and if there’s any kind of security gap, one can easily resolve it in the framework’s websites. Let’s crack open some popular frameworks to help you get started.

Spring
Despite being released almost a decade ago, Spring hasn’t given in to

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While hiring machine learning engineers and data scientists, the companies look for experience related to machine learning projects. A complete machine learning project involves preparing a problem statement, designing a specific solution to the problem, gathering and processing the data, and evaluating the quality of your model.

The machine learning projects you pick up say a lot about your maturity and creativity as a data scientist. If you have the right skillset in your resume, you can pursue a great machine learning career with a good pay scale.

In this guide, we have mentioned some of the best machine learning projects you must have on your portfolio to showcase your machine learning skills to the companies.

Recommender systems
Recommender systems are used everywhere, ranging from movies to products and books. All the medium and large-sized businesses are creating their in-house recommender systems. The most common recommender system is the YouTube Video Recommendation System.

Tools Required – Pytho

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Plan and Design Solutions on OCI
================================






Design, Implement & Operate Databases in OCI
=============================================

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Getting Started with OCI
========================
Global Footprint - 16 active (11 commercial, 5 Govt), 20 new regions (17 commercial, 3 Govt)
Interconnect with Azure - Ashburn and London, other regions planned
OCI Region - Multiple fault de-correlated, completely independent data centers: AD; Grouping of hardware and infra with in AD: FD
One AD Regions - Next 12 months, Region or AD will be added
Off-box Network virtualization - All virtualization put into custom silicon cards, includes all storage and network I/O
OCI Services - Identity, Networking, Compute, Storage, Database, Autonomous DB, Serverless, Analytics, Next Layer Services, Security, Data movement, Edge 
Differentiation - Off-box Network virtualization, Bare metal + Local NVMe storage, All SSD storage, No Network, memory or CPU over-subscription; Battle tested; DB options(BM,VM,Exadata,RAC); Enterprise App support (EBS, JDE)
                  Aggresive and Predictable pricing (Cheaper than AWS); SLAs on Performance, Management and Availability; B

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OCI Architecture:
=================
OCI Regions - 21 Available + 15 Planned; Commercial, Govt, Microsoft Azure Interconnect
Region - Localized Geographical area comprised of 1 or more AD  
Availability Domains - One or more fault-tolerent, isolated DC located within a region, but connected to each other by low latency, high bandwidth network; Do not share physical infra 
Fault Domains - Grouping of hardware and infrastructure with in an AD to provide anti-affinity(logical data center); 3 FD per AD; Do not share SPOHF; change procedures are isolated at FD
One AD Regions - within one year second AD or region will be made available 
Choosing Region - Location, Data Residency & Compliance, Service Availability
Avoid SPOF - Design architecture to deploy instances that perform same tasks in different FD or different AD for multiple AD regions 
Data Guard - Data replication across AD  

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Conditionals Cheat Sheet:
==========================
In earlier videos, we took a look at some of the built-in Python operators that allow us to compare values, and some logical operators we can use to combine values. We also learned how to use operators in if-else-elif blocks. 
It’s a lot to learn but, with practice, it gets easier to remember it all. In the meantime, this handy cheat sheet gives you all the information you need at a glance. 

Comparison operators
a == b: a is equal to b
a != b: a is different than b