• AWS – Amazon API Gateway

  • AWS – Amazon S3 Transfer Acceleration

  • AWS – AWS Snowcone / Snowball / Snowmobile

  • AWS – AWS DataSync

  • AWS – AWS Kinesis Video Streams

  • AWS – AWS Kinesis Data Firehose.

  • AWS – AWS Kinesis Data Streams

  • AWS – How To Redeploy React JS Changes ?

    AWS – How To Redeploy React JS Changes ?

    GenAI – How To Redeploy React JS Changes ? Table Of Contents: Step By Step Process Of Redeploying React JS Changes. Step 1: Build the React App on Local Machine npm run build This generates a fresh /build folder with all updated static files. Step 2: Connect to Your EC2 / VM Use VS Code Remote SSH, or terminal: ssh ubuntu@<your-server-ip> Step 3: Remove Existing Old Files on Server sudo rm -rf /var/www/html/* Step 4: Upload New build Folder to Server sudo cp -r /home/Subrat/build/* /var/www/html/ Step 5: Confirm Upload ls -l /var/www/html/ Ensure you see files like index.html, static/,

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  • AWS – How To Deploy A Python API On An AWS EC2 Instance?

    AWS – How To Deploy A Python API On An AWS EC2 Instance?

    GenAI – How To Deploy A Python API On An AWS EC2 Instance? Table Of Contents: Introduction. Steps To Host React JS App In AWS. (1) Introduction Step-1: Connect to the EC2 Instance Step-2: Navigate To The Project Directory. Move to the directory where your Python API code is located: cd Rahul_R Step-3: Activate The Created Environment Where Your Dependencies Are Installed. Activate the Conda environment where all dependencies are installed: conda activate smart_search_env Step-4: Run the API in the Background Using ‘nohup’ Use nohup to run the API scripts in the background even after the terminal is closed. Redirect

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  • (04) GenAI – Characteristics Of Vector Embeddings.

    (04) GenAI – Characteristics Of Vector Embeddings.

    GenAI – Characteristics Of Vector Embeddings Table Of Contents: What Is Vector Embedding ? Dense Representation. Fixed Dimensionality. Semantic Similarity Is Preserved. Capture Latent Features. Learned From Data. Domain Specific. Efficient For Computation. Compositionality. Continuous & Differentiable. Versatile Uses Across Domains.  (1) What Is Vector Embedding ? (2) Dense Representation (3) Fixed Dimensionality (4) Semantic Similarity is Preserved (5) Captures Latent Features (6) Learned From Data (7) Domain-Specific (8) Efficient for Computation (9) Compositionality (10) Continuous and Differentiable (11) Versatile Usage Across Domains

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