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Marry My Husband Episode 01 Hindi Dubbed -Quiin Media

Marry My Husband Episode 01 Hindi Dubbed -Quiin Media

⏲ 1:6:56 👁 42.2M

Beautiful Art for Beautiful People

Free Background Motion Loop Star Stars Nebula

⏲ 30 seconds 👁 160.1K

heavenly curls

constellations - Jade Lemac (1 HOUR LOOP)

⏲ 1 hour 1 second 👁 21.9K

Marry My Husband Episode 03 Hindi Dubbed -Quiin Media

Marry My Husband Episode 03 Hindi Dubbed -Quiin Media

⏲ 59:12 👁 35.4M

Babert

Galaxy Motion Background Video [HD] ~ FREE

⏲ 11 seconds 👁 196.4K

AA-VFX

5K Shooting Stars ★ 1-Hour Night-Sky ★ Video Background - UHD Animation ║ HD Longest FREE 4K

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Marry My Husband Episode 02 Hindi Dubbed -Quiin Media

Marry My Husband Episode 02 Hindi Dubbed -Quiin Media

⏲ 1:2:1 👁 26.9M

Relaxation Time - 4K Amazing Relaxing Screensavers

Abstract Colorful Constellations! 4K Network Geometric Shapes! Sci-Fi Screensaver for Relaxing

⏲ 11 hours 48 minutes 48 seconds 👁 85.4K

South Florida PBS

\"The Great Star And Constellation Shootout\" 5 Min version

⏲ 5 minutes 6 seconds 👁 286

Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>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.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \

Session 17: Deployment of Machine Learning Models in Open RAN Networks

⏲ 4:9 👁 75K

Phantom Enigma Films

The Most Amazing Nebula Star | Galaxy Universe Space | Relaxing video 8 Hours 1080p full HD

⏲ 8 hours 1 second 👁 853.7K

Celestial Ambience

✨ Stars In The Sky | Ambient Sleep Music | 10 Hours Relaxing Space Travel

⏲ 10 hours 👁 307K

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