TECHNOLOGY 

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KembaraXtra- Computer Terms – 3-nines availability
Three-nines availability describes a system reliability level of 99.9% uptime. This metric indicates that a system is expected to be unavailable for no more than approximately 8.76 hours per year. It is commonly used in service-level agreements (SLAs) to quantify acceptable downtime for networks, servers, and online services.


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KembaraXtra- Computer Terms – 3Station
The 3Station was a diskless workstation developed by Bob Metcalfe at 3Com Coproration.. Designed to operate without local storage, it relied on network resources for booting and application execution. This architecture reduced hardware costs, simplified maintenance, and emphasized centralized management, making it an early example of network-centric computing.


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KembaraXtra- Computer Terms – 400
In the Hypertext Transfer Protocol, 400 is a status code meaning Bad Request. It indicates that a server cannot process a client’s request due to malformed syntax, invalid formatting, or incorrect parameters. This error typically results from client-side issues, such as improperly constructed requests or corrupted data, and signals that the request must be corrected before being resent.


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KembaraXtra- Computer Terms – 401
The 401 Unauthorized HTTP status code indicates that a client request cannot be fulfilled because it lacks valid authentication credentials. The server expects an Authorization header, and without it, access to the requested resource is denied. This response is commonly encountered when accessing protected web resources that require login credentials or tokens.


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KembaraXtra- Computer Terms – 402
The 402 Payment Required HTTP status code signifies that access to a requested resource is contingent upon payment. Although reserved in the HTTP specification and rarely used in practice, it was intended to support digital payment systems by signaling that a transaction could not proceed without appropriate billing information.


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KembaraXtra- Computer Terms – 403
The 403 Forbidden HTTP status code indicates that a server understands a client’s request but refuses to authorize it. Unlike a 401 error, authentication may not resolve the issue, as access is explicitly denied due to permission restrictions, policy rules, or security configurations.


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KembaraXtra- Computer Terms – 404
The 404 Not Found HTTP status code is returned when a server cannot locate a resource that matches the requested URL. This error commonly occurs when a web page has been removed, renamed, or incorrectly referenced. It signals that the server itself is reachable, but the specific content requested does not exist.


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KembaraXtra- Computer Terms – 486
The term 486 refers collectively to Intel’s i486 family of microprocessors. These CPUs represented a significant advancement over the 386 line, integrating features such as on-chip cache memory and improved instruction pipelines, which resulted in better performance and efficiency for personal computers of the era.


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KembaraXtra- Computer Terms – 486DX
The 486DX is a variant of the Intel i486 processor that includes an integrated floating-point unit. This integration allowed faster execution of mathematical operations compared to systems that relied on separate coprocessors, making the 486DX well-suited for graphics, engineering, and scientific applications.


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Artificial Intelligence – Deploying for Online Learning on GCP

Deploying a TensorFlow SavedModel to Google Cloud Platform (GCP) lets us run predictions online, at scale, without managing servers directly.

Your TensorFlow model must be stored in a Google Cloud Storage bucket. This includes the saved_model.pb file.

Step 1: Create a Deployed Model Object

First, create the model container on GCP:

gcloud ml-engine models create "deployed_classifier"

This prepares a container where future versions will live.

Step 2: Tell GCP Where the SavedModel Files Are

Set the environment variable that points to the model binaries:

DEPLOYMENT_SOURCE="gs://classifier_bucket123/classifier_model/binaries"

Step 3: Deploy the Model Version

Deploy a version of your model to Google Cloud ML Engine:

gcloud ml-engine versions create "version1" \
    --model "deployed_classifier" \
    --origin $DEPLOYMENT_SOURCE \
    --runtime-version 1.9

The deployment process may take a few minutes.

Step 4: Prepare Data for Prediction

Define the variables used for prediction:

MODEL_NAME="deployed_classifier"
INPUT_DATA_FILE="test.json"
VERSION_NAME="version1"

The test.json file contains a sample JSON input.

Step 5: Request a Prediction

Send a prediction request to your deployed model:

gcloud ml-engine predict \
    --model $MODEL_NAME \
    --version $VERSION_NAME \
    --json-instances $INPUT_DATA_FILE

You’ll receive JSON output containing the prediction probabilities.

Success! Your Model Is Live □

Your deployed model can now be called from apps, websites, backend services, or automated systems.

For a 10-Year-Old: Super Simple Explanation

You built a smart robot and now you're putting it on the internet.

Here’s what happened:

  • You put the robot’s brain online (Google Cloud Storage).
  • You told Google your robot exists.
  • You told Google where the robot’s brain file is.
  • You turned the robot on (created a version).
  • You asked the robot a question (prediction command).

The robot replies:

  • “This looks like fraud (90% sure)”
  • “This does NOT look like fraud (10% sure)”

Your robot now lives in the cloud and people can ask it questions anytime!

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