Prometheus:

Prometheus is an open-source toolkit written in Go that is designed to be a fully featured solution for monitoring and alerting.

Prometheus is responsible for collecting metrics data and storing it in an efficient time series database, while user is able to query that time-series data and configure alerting for real-time updates.

It actually will use the built-in database within it. We can transfer the data into external storage or keep within it.

It uses a multidimensional data model that accommodates time-series data. This data is associated with the timestamp and options key-value pairs.

Prometheus uses a HTTP pull model to retrieve stored time-series data.

Prometheus Components:

Prometheus server is a responsible for collecting a data from exporter or scraping a data from targeting machines and storing this data into time-series database.

Various client libraries are supported for programming languages including Rust, Python, Java, Ruby and Go. These libraries aid in instrumenting application mode.  Special exporters are also supported for exposing metrics from systems that cannot directly use Prometheus metrics such as Graphite, StatsD and other third parity software.

Load balance is a method of distributing the network traffic equally across the connection/network pool that support an application.

AWS is provided a load balancing called ELB [Elastic Load balancer]

ELB is classified as below:

Basic Networks:

IPv4 is connectless protocol that use multi packet routing to break data into smaller blocks to send across the internet.  IPv4 is a series of of four eight-bit-binary numbers separated by a decimal point. 

IPv4 has a different type of classes:

Creating the VPC network:

Login into AWS console and navigate into VPC.

1)      Select the VPC only option.

We will get a more network option while selecting the VPC and more option. It will display the availability zone along with network CIDR.

2)      Select the IPv4 CIDR manual input in the IPv4 CIDR block section.

If there is an Amazon VPC IP address Manager (IPAM) address pool available in this region, you can get a CIDR from an IPAM pool. If you select an IPAM pool, the size of CIDR is limited by the allocation rules on the IPAM pool. The same will be applicable for IPv6 as well.

3)      Select a default zone, we can specific a different Tenant as well.

4)      Create a tag for grouping purpose and click on create VPC button.

We need to create a sub netmask according your requirement and mapped into VPC.

Click on create subnet button and select the VPC which you want to create a sub netmask.

Define a subnet name and availability zone as per your requirement.

I can able to get 11 number of IP address as per my sub netmask and 5 IP address goes for reservation.

We need to create a internet gateway incase of access this machine from your system or outside world.

Creating an Internet Gateway:

Amazon Web Services is a subsidiary of Amazon that provides on-demand cloud computing platform and API's to Individuals, Companies and Governments on a metered Pay-as-You-go basis.

AWS Global Infrastructure as of 2024:

The AWS Cloud has 108 Availability Zones and 34 launched Regions across the world.  They will plan to extend another 18 more Availability Zones and Six more Regions in Mexico, New Zealand,  the kingdom of Saudi Arabia, Thailand, Taiwan and the AWS European Sovereign Cloud.

Amazon cloud computing resources are hosted in multiple location across world wide.  These location are composed of AWS Regions, Availability Zones and local Zones.  

AWS Regions: Region is a logical name which represent some of the Geographic location.  Each AWS Region has multiple, isolated location knows as Availability Zones.

Local Zones: We can place resources such as compute & storage in multiple locations closer to end user. Local zones are designed to bring core services closer  to end users.

AWS has a multiple resource components for various purpose, we will walk through one by one now.

EC2 - Elastic compute cloud

EC2 provides scalable compute capacity in the AWS cloud. We can create a multiple virtual machines through EC2.  We can scale up or down the resources easily through EC2.

EC2 Instance Purchasing Options:

On-Demand:

* It is very expensive and very flexible purchase options

* We are charged only when instance is running (billed by hour)

* We can terminate or provision at any time.

Reserved:

* Allows us to purchase an instance for a specific time period.

* We can get a special discounts while purchasing it.

Spot:

* Amazon will sell the unused resources with lower price.  

* We can bid on an instances type and get those instance if bid match with our price or go below to our bid price.

* Spot price will be fluctuate based on supply and demand in the market.

The price of EC2 instance will vary based on Instance type, Region, EBS and storage resources.

Amazon Machine Image:

* A preconfigured a package image (ex. ISO) along with necessary software's which require to launch an EC2 instance.

AMI comes with 3 categories as below:

Cloud Service : Any infrastructure service which is make available for user based request via internet.

Cloud Computing: Cloud computing is the delivery of computing services such Server, Storage, Databases, Networking and Software/Analytics over the internet hosted at a remote data center managed by a cloud service provider. 

Instead of buying, owning and maintaining physical data centers and servers, you can access technology services such as computing power, storage and database on an as-need basis from a cloud provider with pay-as-you-go model.

Cloud Service Provider: The company who is providing the cloud computing services to the end  users such as AWS, Azure, GCP, Oracle cloud, Rackspace, DigitalOcean, Saleforce cloud and Alibaba cloud.

Benefits of Cloud:

• Cost
• scalability & Elasticity - We can provision a require amount resources through cloud. We can increase the resources depends requirement basis without downtime via scale out and scale in.
• High Availability & Reliability
• Speed
• Deploy across the global

AI - System or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.  Artificial Intelligence capable of generating text, images, videos or other data using generative models often in response to prompts.   Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

AGI - Artificial General Intelligence is a type of AI that can understand, learn and apply knowledge across broad range of tasks similar to human cognitive abilities.   

Example of Cloud Machine Learning:

Different AI Types:

Machine Learning:

Simple Input -> Simple output -> Single topic

Deep Learning:

Complex input -> Simple output -> Single topic

Foundation Model:

Complex inputs -> Complex ouputs -> multiple topics

Foundation Model:

A foundation model is a type of large scale artificial intelligence model that is trained on a broad range of data at massive scale, allowing to develop a wide understanding of many topics and tasks.  These models can be adapted or fine-tuned for various specific applications, demonstrating flexibility and efficiency across different domains. 

 Parameters of foundation models:

• Embedding Vectors : The foundation model dealing with categorical variable (like words or user ID in recommendation systems), embedding vectors are a form of parameter represent a categories in a continuous vector space and capturing semantic similarities among categories. 
• Weights:  This is a numerous parameters in neural network.  Weights are used in various layers of a neural network to scale the input data in a meaningful ways.  For example in a convolutional layer commonly used in image processing, weights determine the importance of neighboring pixel values for feature detection. 
• Biases: Bias parameters are added to the output of weighted inputs to shift the activation function curve up or down.  This is crucial for models to accurately represent patterns in data that do not pass through the origin of the coordinate system. 
• Attention Scores: Attention mechanisms use parameters to weigh the significance of different parts of the input data differently.  For instance, in language models attention scores determine how much emphasis the model places on different words when generating a response or translating text.