Bikini Fashion Show: New General (ft. Sentuna) Playlist Edition|| Barbara Palvin Modeling -Alexara from ftv model s Watch Video

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✓ Published: 05-Jun-2024
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Alexara is one of the International leading Brand.<br/>We are selling different collection of Bikinis, Lingerie, Swim Wears, Rash Guards, Sleeping and Swim wears clothes.<br/>Alexara also working on Music Blog & dedicated to promoting new Artists.

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Alexara is one of the International leading Brand.&#60;br/&#62;We are selling different collection of Bikinis, Lingerie, Swim Wears, Rash Guards, Sleeping and Swim wears clothes.&#60;br/&#62;Alexara also working on Music Blog &amp; dedicated to promoting new Artists.
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Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we&#39;ll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.&#60;br/&#62;&#60;br/&#62;Deployment Example:&#60;br/&#62;Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.&#60;br/&#62;&#60;br/&#62;Deployment Steps:&#60;br/&#62;&#60;br/&#62;1. Data Collection and Preprocessing:&#60;br/&#62;The operator collects historical traffic data, including throughput, latency, and user traffic patterns.&#60;br/&#62;They preprocess the data to remove outliers and normalize features.&#60;br/&#62;&#60;br/&#62;2. Model Development:&#60;br/&#62;Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.&#60;br/&#62;They use a development environment with libraries like TensorFlow or scikit-learn for model development.&#60;br/&#62;&#60;br/&#62;3. Offline Model Training and Validation (Loop 1):&#60;br/&#62;The model is trained on historical data using algorithms like linear regression or decision trees.&#60;br/&#62;Validation is done using a separate dataset to ensure the model&#39;s accuracy.&#60;br/&#62;&#60;br/&#62;4. Online Model Deployment and Monitoring (Loop 2):&#60;br/&#62;Once validated, the model is deployed in the network&#39;s edge servers or cloud infrastructure.&#60;br/&#62;Real-time network data, such as current traffic conditions, is fed into the model for predictions.&#60;br/&#62;Model performance is monitored using metrics like prediction accuracy and latency.&#60;br/&#62;&#60;br/&#62;5. Closed-Loop Automation (Loop 3):&#60;br/&#62;The model&#39;s predictions are used by the network&#39;s orchestration and automation tools to dynamically allocate resources.&#60;br/&#62;For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.&#60;br/&#62;&#60;br/&#62;Subscribe to &#92;
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